Struct KalmanODProcess 

pub struct KalmanODProcess<D: Dynamics, MsrSize: DimName, Accel: DimName, Trk: TrackerSensitivity<D::StateType, D::StateType>>
where
    D::StateType: Interpolatable + Add<OVector<f64, <D::StateType as State>::Size>, Output = D::StateType>,
    <DefaultAllocator as Allocator<<D::StateType as State>::VecLength>>::Buffer<f64>: Send,
    <DefaultAllocator as Allocator<<D::StateType as State>::Size>>::Buffer<f64>: Copy,
    <DefaultAllocator as Allocator<<D::StateType as State>::Size, <D::StateType as State>::Size>>::Buffer<f64>: Copy,
    DefaultAllocator: Allocator<<D::StateType as State>::Size> + Allocator<<D::StateType as State>::VecLength> + Allocator<MsrSize> + Allocator<MsrSize, <D::StateType as State>::Size> + Allocator<<D::StateType as State>::Size, MsrSize> + Allocator<MsrSize, MsrSize> + Allocator<<D::StateType as State>::Size, <D::StateType as State>::Size> + Allocator<Accel> + Allocator<Accel, Accel> + Allocator<<D::StateType as State>::Size, Accel> + Allocator<Accel, <D::StateType as State>::Size>,
{
    pub prop: Propagator<D>,
    pub kf_variant: KalmanVariant,
    pub resid_crit: Option<ResidRejectCrit>,
    pub devices: BTreeMap<String, Trk>,
    pub process_noise: Vec<ProcessNoise<Accel>>,
    pub max_step: Duration,
    pub epoch_precision: Duration,
    pub almanac: Arc<Almanac>,
    /* private fields */
}

An orbit determination process (ODP) which filters OD measurements through a Kalman filter.

Fields

prop: Propagator<D>

Propagator used for the estimation

kf_variant: KalmanVariant

Kalman filter variant

resid_crit: Option<ResidRejectCrit>

Residual rejection criteria allows preventing bad measurements from affecting the estimation.

devices: BTreeMap<String, Trk>

Tracking devices

process_noise: Vec<ProcessNoise<Accel>>

A sets of process noise (usually noted Q), must be ordered chronologically

max_step: Duration

Maximum step size where the STM linearization is assumed correct (1 minute is usually fine)

epoch_precision: Duration

Precision of the measurement epoch when processing measurements.

almanac: Arc<Almanac>

Implementations

impl<D: Dynamics, MsrSize: DimName, Accel: DimName, Trk: TrackerSensitivity<D::StateType, D::StateType>> KalmanODProcess<D, MsrSize, Accel, Trk>

where D::StateType: Interpolatable + Add<OVector<f64, <D::StateType as State>::Size>, Output = D::StateType>, <DefaultAllocator as Allocator<<D::StateType as State>::VecLength>>::Buffer<f64>: Send, <DefaultAllocator as Allocator<<D::StateType as State>::Size>>::Buffer<f64>: Copy, <DefaultAllocator as Allocator<<D::StateType as State>::Size, <D::StateType as State>::Size>>::Buffer<f64>: Copy, DefaultAllocator: Allocator<<D::StateType as State>::Size> + Allocator<<D::StateType as State>::VecLength> + Allocator<MsrSize> + Allocator<MsrSize, <D::StateType as State>::Size> + Allocator<<D::StateType as State>::Size, MsrSize> + Allocator<MsrSize, MsrSize> + Allocator<<D::StateType as State>::Size, <D::StateType as State>::Size> + Allocator<Accel> + Allocator<Accel, Accel> + Allocator<<D::StateType as State>::Size, Accel> + Allocator<Accel, <D::StateType as State>::Size> + Allocator<Const<1>, MsrSize>,

pub fn new( prop: Propagator<D>, kf_variant: KalmanVariant, resid_crit: Option<ResidRejectCrit>, devices: BTreeMap<String, Trk>, almanac: Arc<Almanac>, ) -> Self

Initialize a new Kalman sequential filter for the orbit determination process, setting the max step of the STM to one minute, and the measurement epoch precision to 1 microsecond.

Examples found in repository

examples/02_jwst_covar_monte_carlo/main.rs (lines 116-122)

fn main() -> Result<(), Box<dyn Error>> {
    pel::init();
    // Dynamics models require planetary constants and ephemerides to be defined.
    // Let's start by grabbing those by using ANISE's latest MetaAlmanac.
    // For details, refer to https://github.com/nyx-space/anise/blob/master/data/latest.dhall.

    // Download the regularly update of the James Webb Space Telescope reconstucted (or definitive) ephemeris.
    // Refer to https://naif.jpl.nasa.gov/pub/naif/JWST/kernels/spk/aareadme.txt for details.
    let mut latest_jwst_ephem = MetaFile {
        uri: "https://naif.jpl.nasa.gov/pub/naif/JWST/kernels/spk/jwst_rec.bsp".to_string(),
        crc32: None,
    };
    latest_jwst_ephem.process(true)?;

    // Load this ephem in the general Almanac we're using for this analysis.
    let almanac = Arc::new(
        MetaAlmanac::latest()
            .map_err(Box::new)?
            .load_from_metafile(latest_jwst_ephem, true)?,
    );

    // By loading this ephemeris file in the ANISE GUI or ANISE CLI, we can find the NAIF ID of the JWST
    // in the BSP. We need this ID in order to query the ephemeris.
    const JWST_NAIF_ID: i32 = -170;
    // Let's build a frame in the J2000 orientation centered on the JWST.
    const JWST_J2000: Frame = Frame::from_ephem_j2000(JWST_NAIF_ID);

    // Since the ephemeris file is updated regularly, we'll just grab the latest state in the ephem.
    let (earliest_epoch, latest_epoch) = almanac.spk_domain(JWST_NAIF_ID)?;
    println!("JWST defined from {earliest_epoch} to {latest_epoch}");
    // Fetch the state, printing it in the Earth J2000 frame.
    let jwst_orbit = almanac.transform(JWST_J2000, EARTH_J2000, latest_epoch, None)?;
    println!("{jwst_orbit:x}");

    // Build the spacecraft
    // SRP area assumed to be the full sunshield and mass if 6200.0 kg, c.f. https://webb.nasa.gov/content/about/faqs/facts.html
    // SRP Coefficient of reflectivity assumed to be that of Kapton, i.e. 2 - 0.44 = 1.56, table 1 from https://amostech.com/TechnicalPapers/2018/Poster/Bengtson.pdf
    let jwst = Spacecraft::builder()
        .orbit(jwst_orbit)
        .srp(SRPData {
            area_m2: 21.197 * 14.162,
            coeff_reflectivity: 1.56,
        })
        .mass(Mass::from_dry_mass(6200.0))
        .build();

    // Build up the spacecraft uncertainty builder.
    // We can use the spacecraft uncertainty structure to build this up.
    // We start by specifying the nominal state (as defined above), then the uncertainty in position and velocity
    // in the RIC frame. We could also specify the Cr, Cd, and mass uncertainties, but these aren't accounted for until
    // Nyx can also estimate the deviation of the spacecraft parameters.
    let jwst_uncertainty = SpacecraftUncertainty::builder()
        .nominal(jwst)
        .frame(LocalFrame::RIC)
        .x_km(0.5)
        .y_km(0.3)
        .z_km(1.5)
        .vx_km_s(1e-4)
        .vy_km_s(0.6e-3)
        .vz_km_s(3e-3)
        .build();

    println!("{jwst_uncertainty}");

    // Build the Kalman filter estimate.
    // Note that we could have used the KfEstimate structure directly (as seen throughout the OD integration tests)
    // but this approach requires quite a bit more boilerplate code.
    let jwst_estimate = jwst_uncertainty.to_estimate()?;

    // Set up the spacecraft dynamics.
    // We'll use the point masses of the Earth, Sun, Jupiter (barycenter, because it's in the DE440), and the Moon.
    // We'll also enable solar radiation pressure since the James Webb has a huge and highly reflective sun shield.

    let orbital_dyn = OrbitalDynamics::point_masses(vec![MOON, SUN, JUPITER_BARYCENTER]);
    let srp_dyn = SolarPressure::new(vec![EARTH_J2000, MOON_J2000], almanac.clone())?;

    // Finalize setting up the dynamics.
    let dynamics = SpacecraftDynamics::from_model(orbital_dyn, srp_dyn);

    // Build the propagator set up to use for the whole analysis.
    let setup = Propagator::default(dynamics);

    // All of the analysis will use this duration.
    let prediction_duration = 6.5 * Unit::Day;

    // === Covariance mapping ===
    // For the covariance mapping / prediction, we'll use the common orbit determination approach.
    // This is done by setting up a spacecraft Kalman filter OD process, and predicting for the analysis duration.

    // Build the propagation instance for the OD process.
    let odp = SpacecraftKalmanOD::new(
        setup.clone(),
        KalmanVariant::DeviationTracking,
        None,
        BTreeMap::new(),
        almanac.clone(),
    );

    // The prediction step is 1 minute by default, configured in the OD process, i.e. how often we want to know the covariance.
    assert_eq!(odp.max_step, 1_i64.minutes());
    // Finally, predict, and export the trajectory with covariance to a parquet file.
    let od_sol = odp.predict_for(jwst_estimate, prediction_duration)?;
    od_sol.to_parquet("./02_jwst_covar_map.parquet", ExportCfg::default())?;

    // === Monte Carlo framework ===
    // Nyx comes with a complete multi-threaded Monte Carlo frame. It's blazing fast.

    let my_mc = MonteCarlo::new(
        jwst, // Nominal state
        jwst_estimate.to_random_variable()?,
        "02_jwst".to_string(), // Scenario name
        None, // No specific seed specified, so one will be drawn from the computer's entropy.
    );

    let num_runs = 5_000;
    let rslts = my_mc.run_until_epoch(
        setup,
        almanac.clone(),
        jwst.epoch() + prediction_duration,
        num_runs,
    );

    assert_eq!(rslts.runs.len(), num_runs);
    // Finally, export these results, computing the eclipse percentage for all of these results.

    // For all of the resulting trajectories, we'll want to compute the percentage of penumbra and umbra.
    let eclipse_loc = EclipseLocator::cislunar(almanac.clone());
    let umbra_event = eclipse_loc.to_umbra_event();
    let penumbra_event = eclipse_loc.to_penumbra_event();

    rslts.to_parquet(
        "02_jwst_monte_carlo.parquet",
        Some(vec![&umbra_event, &penumbra_event]),
        ExportCfg::default(),
        almanac,
    )?;

    Ok(())
}

examples/05_cislunar_spacecraft_link_od/main.rs (lines 217-223)

fn main() -> Result<(), Box<dyn Error>> {
    pel::init();

    // ====================== //
    // === ALMANAC SET UP === //
    // ====================== //

    let manifest_dir =
        PathBuf::from(std::env::var("CARGO_MANIFEST_DIR").unwrap_or(".".to_string()));

    let out = manifest_dir.join("data/04_output/");

    let almanac = Arc::new(
        Almanac::new(
            &manifest_dir
                .join("data/01_planetary/pck08.pca")
                .to_string_lossy(),
        )
        .unwrap()
        .load(
            &manifest_dir
                .join("data/01_planetary/de440s.bsp")
                .to_string_lossy(),
        )
        .unwrap(),
    );

    let eme2k = almanac.frame_info(EARTH_J2000).unwrap();
    let moon_iau = almanac.frame_info(IAU_MOON_FRAME).unwrap();

    let epoch = Epoch::from_gregorian_tai(2021, 5, 29, 19, 51, 16, 852_000);
    let nrho = Orbit::cartesian(
        166_473.631_302_239_7,
        -274_715.487_253_382_7,
        -211_233.210_176_686_7,
        0.933_451_604_520_018_4,
        0.436_775_046_841_900_9,
        -0.082_211_021_250_348_95,
        epoch,
        eme2k,
    );

    let tx_nrho_sc = Spacecraft::from(nrho);

    let state_luna = almanac.transform_to(nrho, MOON_J2000, None).unwrap();
    println!("Start state (dynamics: Earth, Moon, Sun gravity):\n{state_luna}");

    let bodies = vec![EARTH, SUN];
    let dynamics = SpacecraftDynamics::new(OrbitalDynamics::point_masses(bodies));

    let setup = Propagator::rk89(
        dynamics,
        IntegratorOptions::builder().max_step(0.5.minutes()).build(),
    );

    /* == Propagate the NRHO vehicle == */
    let prop_time = 1.1 * state_luna.period().unwrap();

    let (nrho_final, mut tx_traj) = setup
        .with(tx_nrho_sc, almanac.clone())
        .for_duration_with_traj(prop_time)
        .unwrap();

    tx_traj.name = Some("NRHO Tx SC".to_string());

    println!("{tx_traj}");

    /* == Propagate an LLO vehicle == */
    let llo_orbit =
        Orbit::try_keplerian_altitude(110.0, 1e-4, 90.0, 0.0, 0.0, 0.0, epoch, moon_iau).unwrap();

    let llo_sc = Spacecraft::builder().orbit(llo_orbit).build();

    let (_, llo_traj) = setup
        .with(llo_sc, almanac.clone())
        .until_epoch_with_traj(nrho_final.epoch())
        .unwrap();

    // Export the subset of the first two hours.
    llo_traj
        .clone()
        .filter_by_offset(..2.hours())
        .to_parquet_simple(out.join("05_caps_llo_truth.pq"), almanac.clone())?;

    /* == Setup the interlink == */

    let mut measurement_types = IndexSet::new();
    measurement_types.insert(MeasurementType::Range);
    measurement_types.insert(MeasurementType::Doppler);

    let mut stochastics = IndexMap::new();

    let sa45_csac_allan_dev = 1e-11;

    stochastics.insert(
        MeasurementType::Range,
        StochasticNoise::from_hardware_range_km(
            sa45_csac_allan_dev,
            10.0.seconds(),
            link_specific::ChipRate::StandardT4B,
            link_specific::SN0::Average,
        ),
    );

    stochastics.insert(
        MeasurementType::Doppler,
        StochasticNoise::from_hardware_doppler_km_s(
            sa45_csac_allan_dev,
            10.0.seconds(),
            link_specific::CarrierFreq::SBand,
            link_specific::CN0::Average,
        ),
    );

    let interlink = InterlinkTxSpacecraft {
        traj: tx_traj,
        measurement_types,
        integration_time: None,
        timestamp_noise_s: None,
        ab_corr: Aberration::LT,
        stochastic_noises: Some(stochastics),
    };

    // Devices are the transmitter, which is our NRHO vehicle.
    let mut devices = BTreeMap::new();
    devices.insert("NRHO Tx SC".to_string(), interlink);

    let mut configs = BTreeMap::new();
    configs.insert(
        "NRHO Tx SC".to_string(),
        TrkConfig::builder()
            .strands(vec![Strand {
                start: epoch,
                end: nrho_final.epoch(),
            }])
            .build(),
    );

    let mut trk_sim =
        TrackingArcSim::with_seed(devices.clone(), llo_traj.clone(), configs, 0).unwrap();
    println!("{trk_sim}");

    let trk_data = trk_sim.generate_measurements(almanac.clone()).unwrap();
    println!("{trk_data}");

    trk_data
        .to_parquet_simple(out.clone().join("nrho_interlink_msr.pq"))
        .unwrap();

    // Run a truth OD where we estimate the LLO position
    let llo_uncertainty = SpacecraftUncertainty::builder()
        .nominal(llo_sc)
        .x_km(1.0)
        .y_km(1.0)
        .z_km(1.0)
        .vx_km_s(1e-3)
        .vy_km_s(1e-3)
        .vz_km_s(1e-3)
        .build();

    let mut proc_devices = devices.clone();

    // Define the initial estimate, randomized, seed for reproducibility
    let mut initial_estimate = llo_uncertainty.to_estimate_randomized(Some(0)).unwrap();
    // Inflate the covariance -- https://github.com/nyx-space/nyx/issues/339
    initial_estimate.covar *= 2.5;

    // Increase the noise in the devices to accept more measurements.

    for link in proc_devices.values_mut() {
        for noise in &mut link.stochastic_noises.as_mut().unwrap().values_mut() {
            *noise.white_noise.as_mut().unwrap() *= 3.0;
        }
    }

    let init_err = initial_estimate
        .orbital_state()
        .ric_difference(&llo_orbit)
        .unwrap();

    println!("initial estimate:\n{initial_estimate}");
    println!("RIC errors = {init_err}",);

    let odp = InterlinkKalmanOD::new(
        setup.clone(),
        KalmanVariant::ReferenceUpdate,
        Some(ResidRejectCrit::default()),
        proc_devices,
        almanac.clone(),
    );

    // Shrink the data to process.
    let arc = trk_data.filter_by_offset(..2.hours());

    let od_sol = odp.process_arc(initial_estimate, &arc).unwrap();

    println!("{od_sol}");

    od_sol
        .to_parquet(
            out.join("05_caps_interlink_od_sol.pq"),
            ExportCfg::default(),
        )
        .unwrap();

    let od_traj = od_sol.to_traj().unwrap();

    od_traj
        .ric_diff_to_parquet(
            &llo_traj,
            out.join("05_caps_interlink_llo_est_error.pq"),
            ExportCfg::default(),
        )
        .unwrap();

    let final_est = od_sol.estimates.last().unwrap();
    assert!(final_est.within_3sigma(), "should be within 3 sigma");

    println!("ESTIMATE\n{final_est:x}\n");
    let truth = llo_traj.at(final_est.epoch()).unwrap();
    println!("TRUTH\n{truth:x}");

    let final_err = truth
        .orbit
        .ric_difference(&final_est.orbital_state())
        .unwrap();
    println!("ERROR {final_err}");

    // Build the residuals versus reference plot.
    let rvr_sol = odp
        .process_arc(initial_estimate, &arc.resid_vs_ref_check())
        .unwrap();

    rvr_sol
        .to_parquet(
            out.join("05_caps_interlink_resid_v_ref.pq"),
            ExportCfg::default(),
        )
        .unwrap();

    let final_rvr = rvr_sol.estimates.last().unwrap();

    println!("RMAG error {:.3} m", final_err.rmag_km() * 1e3);
    println!(
        "Pure prop error {:.3} m",
        final_rvr
            .orbital_state()
            .ric_difference(&final_est.orbital_state())
            .unwrap()
            .rmag_km()
            * 1e3
    );

    Ok(())
}

examples/04_lro_od/main.rs (lines 278-284)

fn main() -> Result<(), Box<dyn Error>> {
    pel::init();

    // ====================== //
    // === ALMANAC SET UP === //
    // ====================== //

    // Dynamics models require planetary constants and ephemerides to be defined.
    // Let's start by grabbing those by using ANISE's MetaAlmanac.

    let data_folder: PathBuf = [env!("CARGO_MANIFEST_DIR"), "examples", "04_lro_od"]
        .iter()
        .collect();

    let meta = data_folder.join("lro-dynamics.dhall");

    // Load this ephem in the general Almanac we're using for this analysis.
    let mut almanac = MetaAlmanac::new(meta.to_string_lossy().as_ref())
        .map_err(Box::new)?
        .process(true)
        .map_err(Box::new)?;

    let mut moon_pc = almanac.planetary_data.get_by_id(MOON)?;
    moon_pc.mu_km3_s2 = 4902.74987;
    almanac.planetary_data.set_by_id(MOON, moon_pc)?;

    let mut earth_pc = almanac.planetary_data.get_by_id(EARTH)?;
    earth_pc.mu_km3_s2 = 398600.436;
    almanac.planetary_data.set_by_id(EARTH, earth_pc)?;

    // Save this new kernel for reuse.
    // In an operational context, this would be part of the "Lock" process, and should not change throughout the mission.
    almanac
        .planetary_data
        .save_as(&data_folder.join("lro-specific.pca"), true)?;

    // Lock the almanac (an Arc is a read only structure).
    let almanac = Arc::new(almanac);

    // Orbit determination requires a Trajectory structure, which can be saved as parquet file.
    // In our case, the trajectory comes from the BSP file, so we need to build a Trajectory from the almanac directly.
    // To query the Almanac, we need to build the LRO frame in the J2000 orientation in our case.
    // Inspecting the LRO BSP in the ANISE GUI shows us that NASA has assigned ID -85 to LRO.
    let lro_frame = Frame::from_ephem_j2000(-85);

    // To build the trajectory we need to provide a spacecraft template.
    let sc_template = Spacecraft::builder()
        .mass(Mass::from_dry_and_prop_masses(1018.0, 900.0)) // Launch masses
        .srp(SRPData {
            // SRP configuration is arbitrary, but we will be estimating it anyway.
            area_m2: 3.9 * 2.7,
            coeff_reflectivity: 0.96,
        })
        .orbit(Orbit::zero(MOON_J2000)) // Setting a zero orbit here because it's just a template
        .build();
    // Now we can build the trajectory from the BSP file.
    // We'll arbitrarily set the tracking arc to 24 hours with a five second time step.
    let traj_as_flown = Traj::from_bsp(
        lro_frame,
        MOON_J2000,
        almanac.clone(),
        sc_template,
        5.seconds(),
        Some(Epoch::from_str("2024-01-01 00:00:00 UTC")?),
        Some(Epoch::from_str("2024-01-02 00:00:00 UTC")?),
        Aberration::LT,
        Some("LRO".to_string()),
    )?;

    println!("{traj_as_flown}");

    // ====================== //
    // === MODEL MATCHING === //
    // ====================== //

    // Set up the spacecraft dynamics.

    // Specify that the orbital dynamics must account for the graviational pull of the Earth and the Sun.
    // The gravity of the Moon will also be accounted for since the spaceraft in a lunar orbit.
    let mut orbital_dyn = OrbitalDynamics::point_masses(vec![EARTH, SUN, JUPITER_BARYCENTER]);

    // We want to include the spherical harmonics, so let's download the gravitational data from the Nyx Cloud.
    // We're using the GRAIL JGGRX model.
    let mut jggrx_meta = MetaFile {
        uri: "http://public-data.nyxspace.com/nyx/models/Luna_jggrx_1500e_sha.tab.gz".to_string(),
        crc32: Some(0x6bcacda8), // Specifying the CRC32 avoids redownloading it if it's cached.
    };
    // And let's download it if we don't have it yet.
    jggrx_meta.process(true)?;

    // Build the spherical harmonics.
    // The harmonics must be computed in the body fixed frame.
    // We're using the long term prediction of the Moon principal axes frame.
    let moon_pa_frame = MOON_PA_FRAME.with_orient(31008);
    let sph_harmonics = Harmonics::from_stor(
        almanac.frame_info(moon_pa_frame)?,
        HarmonicsMem::from_shadr(&jggrx_meta.uri, 80, 80, true)?,
    );

    // Include the spherical harmonics into the orbital dynamics.
    orbital_dyn.accel_models.push(sph_harmonics);

    // We define the solar radiation pressure, using the default solar flux and accounting only
    // for the eclipsing caused by the Earth and Moon.
    // Note that by default, enabling the SolarPressure model will also enable the estimation of the coefficient of reflectivity.
    let srp_dyn = SolarPressure::new(vec![EARTH_J2000, MOON_J2000], almanac.clone())?;

    // Finalize setting up the dynamics, specifying the force models (orbital_dyn) separately from the
    // acceleration models (SRP in this case). Use `from_models` to specify multiple accel models.
    let dynamics = SpacecraftDynamics::from_model(orbital_dyn, srp_dyn);

    println!("{dynamics}");

    // Now we can build the propagator.
    let setup = Propagator::default_dp78(dynamics.clone());

    // For reference, let's build the trajectory with Nyx's models from that LRO state.
    let (sim_final, traj_as_sim) = setup
        .with(*traj_as_flown.first(), almanac.clone())
        .until_epoch_with_traj(traj_as_flown.last().epoch())?;

    println!("SIM INIT:  {:x}", traj_as_flown.first());
    println!("SIM FINAL: {sim_final:x}");
    // Compute RIC difference between SIM and LRO ephem
    let sim_lro_delta = sim_final
        .orbit
        .ric_difference(&traj_as_flown.last().orbit)?;
    println!("{traj_as_sim}");
    println!(
        "SIM v LRO - RIC Position (m): {:.3}",
        sim_lro_delta.radius_km * 1e3
    );
    println!(
        "SIM v LRO - RIC Velocity (m/s): {:.3}",
        sim_lro_delta.velocity_km_s * 1e3
    );

    traj_as_sim.ric_diff_to_parquet(
        &traj_as_flown,
        "./04_lro_sim_truth_error.parquet",
        ExportCfg::default(),
    )?;

    // ==================== //
    // === OD SIMULATOR === //
    // ==================== //

    // After quite some time trying to exactly match the model, we still end up with an oscillatory difference on the order of 150 meters between the propagated state
    // and the truth LRO state.

    // Therefore, we will actually run an estimation from a dispersed LRO state.
    // The sc_seed is the true LRO state from the BSP.
    let sc_seed = *traj_as_flown.first();

    // Load the Deep Space Network ground stations.
    // Nyx allows you to build these at runtime but it's pretty static so we can just load them from YAML.
    let ground_station_file: PathBuf = [
        env!("CARGO_MANIFEST_DIR"),
        "examples",
        "04_lro_od",
        "dsn-network.yaml",
    ]
    .iter()
    .collect();

    let devices = GroundStation::load_named(ground_station_file)?;

    let mut proc_devices = devices.clone();

    // Increase the noise in the devices to accept more measurements.
    for gs in proc_devices.values_mut() {
        if let Some(noise) = &mut gs
            .stochastic_noises
            .as_mut()
            .unwrap()
            .get_mut(&MeasurementType::Range)
        {
            *noise.white_noise.as_mut().unwrap() *= 3.0;
        }
    }

    // Typical OD software requires that you specify your own tracking schedule or you'll have overlapping measurements.
    // Nyx can build a tracking schedule for you based on the first station with access.
    let trkconfg_yaml: PathBuf = [
        env!("CARGO_MANIFEST_DIR"),
        "examples",
        "04_lro_od",
        "tracking-cfg.yaml",
    ]
    .iter()
    .collect();

    let configs: BTreeMap<String, TrkConfig> = TrkConfig::load_named(trkconfg_yaml)?;

    // Build the tracking arc simulation to generate a "standard measurement".
    let mut trk = TrackingArcSim::<Spacecraft, GroundStation>::with_seed(
        devices.clone(),
        traj_as_flown.clone(),
        configs,
        123, // Set a seed for reproducibility
    )?;

    trk.build_schedule(almanac.clone())?;
    let arc = trk.generate_measurements(almanac.clone())?;
    // Save the simulated tracking data
    arc.to_parquet_simple("./04_lro_simulated_tracking.parquet")?;

    // We'll note that in our case, we have continuous coverage of LRO when the vehicle is not behind the Moon.
    println!("{arc}");

    // Now that we have simulated measurements, we'll run the orbit determination.

    // ===================== //
    // === OD ESTIMATION === //
    // ===================== //

    let sc = SpacecraftUncertainty::builder()
        .nominal(sc_seed)
        .frame(LocalFrame::RIC)
        .x_km(0.5)
        .y_km(0.5)
        .z_km(0.5)
        .vx_km_s(5e-3)
        .vy_km_s(5e-3)
        .vz_km_s(5e-3)
        .build();

    // Build the filter initial estimate, which we will reuse in the filter.
    let mut initial_estimate = sc.to_estimate()?;
    initial_estimate.covar *= 3.0;

    println!("== FILTER STATE ==\n{sc_seed:x}\n{initial_estimate}");

    // Build the SNC in the Moon J2000 frame, specified as a velocity noise over time.
    let process_noise = ProcessNoise3D::from_velocity_km_s(
        &[1e-10, 1e-10, 1e-10],
        1 * Unit::Hour,
        10 * Unit::Minute,
        None,
    );

    println!("{process_noise}");

    // We'll set up the OD process to reject measurements whose residuals are move than 3 sigmas away from what we expect.
    let odp = SpacecraftKalmanOD::new(
        setup,
        KalmanVariant::ReferenceUpdate,
        Some(ResidRejectCrit::default()),
        proc_devices,
        almanac.clone(),
    )
    .with_process_noise(process_noise);

    let od_sol = odp.process_arc(initial_estimate, &arc)?;

    let final_est = od_sol.estimates.last().unwrap();

    println!("{final_est}");

    let ric_err = traj_as_flown
        .at(final_est.epoch())?
        .orbit
        .ric_difference(&final_est.orbital_state())?;
    println!("== RIC at end ==");
    println!("RIC Position (m): {:.3}", ric_err.radius_km * 1e3);
    println!("RIC Velocity (m/s): {:.3}", ric_err.velocity_km_s * 1e3);

    println!(
        "Num residuals rejected: #{}",
        od_sol.rejected_residuals().len()
    );
    println!(
        "Percentage within +/-3: {}",
        od_sol.residual_ratio_within_threshold(3.0).unwrap()
    );
    println!("Ratios normal? {}", od_sol.is_normal(None).unwrap());

    od_sol.to_parquet("./04_lro_od_results.parquet", ExportCfg::default())?;

    // In our case, we have the truth trajectory from NASA.
    // So we can compute the RIC state difference between the real LRO ephem and what we've just estimated.
    // Export the OD trajectory first.
    let od_trajectory = od_sol.to_traj()?;
    // Build the RIC difference.
    od_trajectory.ric_diff_to_parquet(
        &traj_as_flown,
        "./04_lro_od_truth_error.parquet",
        ExportCfg::default(),
    )?;

    Ok(())
}

pub fn from_process_noise( prop: Propagator<D>, kf_variant: KalmanVariant, devices: BTreeMap<String, Trk>, resid_crit: Option<ResidRejectCrit>, process_noise: ProcessNoise<Accel>, almanac: Arc<Almanac>, ) -> Self

Set (or replaces) the existing process noise configuration.

pub fn with_process_noise(self, process_noise: ProcessNoise<Accel>) -> Self

Set (or replaces) the existing process noise configuration.

Examples found in repository

examples/04_lro_od/main.rs (line 285)

fn main() -> Result<(), Box<dyn Error>> {
    pel::init();

    // ====================== //
    // === ALMANAC SET UP === //
    // ====================== //

    // Dynamics models require planetary constants and ephemerides to be defined.
    // Let's start by grabbing those by using ANISE's MetaAlmanac.

    let data_folder: PathBuf = [env!("CARGO_MANIFEST_DIR"), "examples", "04_lro_od"]
        .iter()
        .collect();

    let meta = data_folder.join("lro-dynamics.dhall");

    // Load this ephem in the general Almanac we're using for this analysis.
    let mut almanac = MetaAlmanac::new(meta.to_string_lossy().as_ref())
        .map_err(Box::new)?
        .process(true)
        .map_err(Box::new)?;

    let mut moon_pc = almanac.planetary_data.get_by_id(MOON)?;
    moon_pc.mu_km3_s2 = 4902.74987;
    almanac.planetary_data.set_by_id(MOON, moon_pc)?;

    let mut earth_pc = almanac.planetary_data.get_by_id(EARTH)?;
    earth_pc.mu_km3_s2 = 398600.436;
    almanac.planetary_data.set_by_id(EARTH, earth_pc)?;

    // Save this new kernel for reuse.
    // In an operational context, this would be part of the "Lock" process, and should not change throughout the mission.
    almanac
        .planetary_data
        .save_as(&data_folder.join("lro-specific.pca"), true)?;

    // Lock the almanac (an Arc is a read only structure).
    let almanac = Arc::new(almanac);

    // Orbit determination requires a Trajectory structure, which can be saved as parquet file.
    // In our case, the trajectory comes from the BSP file, so we need to build a Trajectory from the almanac directly.
    // To query the Almanac, we need to build the LRO frame in the J2000 orientation in our case.
    // Inspecting the LRO BSP in the ANISE GUI shows us that NASA has assigned ID -85 to LRO.
    let lro_frame = Frame::from_ephem_j2000(-85);

    // To build the trajectory we need to provide a spacecraft template.
    let sc_template = Spacecraft::builder()
        .mass(Mass::from_dry_and_prop_masses(1018.0, 900.0)) // Launch masses
        .srp(SRPData {
            // SRP configuration is arbitrary, but we will be estimating it anyway.
            area_m2: 3.9 * 2.7,
            coeff_reflectivity: 0.96,
        })
        .orbit(Orbit::zero(MOON_J2000)) // Setting a zero orbit here because it's just a template
        .build();
    // Now we can build the trajectory from the BSP file.
    // We'll arbitrarily set the tracking arc to 24 hours with a five second time step.
    let traj_as_flown = Traj::from_bsp(
        lro_frame,
        MOON_J2000,
        almanac.clone(),
        sc_template,
        5.seconds(),
        Some(Epoch::from_str("2024-01-01 00:00:00 UTC")?),
        Some(Epoch::from_str("2024-01-02 00:00:00 UTC")?),
        Aberration::LT,
        Some("LRO".to_string()),
    )?;

    println!("{traj_as_flown}");

    // ====================== //
    // === MODEL MATCHING === //
    // ====================== //

    // Set up the spacecraft dynamics.

    // Specify that the orbital dynamics must account for the graviational pull of the Earth and the Sun.
    // The gravity of the Moon will also be accounted for since the spaceraft in a lunar orbit.
    let mut orbital_dyn = OrbitalDynamics::point_masses(vec![EARTH, SUN, JUPITER_BARYCENTER]);

    // We want to include the spherical harmonics, so let's download the gravitational data from the Nyx Cloud.
    // We're using the GRAIL JGGRX model.
    let mut jggrx_meta = MetaFile {
        uri: "http://public-data.nyxspace.com/nyx/models/Luna_jggrx_1500e_sha.tab.gz".to_string(),
        crc32: Some(0x6bcacda8), // Specifying the CRC32 avoids redownloading it if it's cached.
    };
    // And let's download it if we don't have it yet.
    jggrx_meta.process(true)?;

    // Build the spherical harmonics.
    // The harmonics must be computed in the body fixed frame.
    // We're using the long term prediction of the Moon principal axes frame.
    let moon_pa_frame = MOON_PA_FRAME.with_orient(31008);
    let sph_harmonics = Harmonics::from_stor(
        almanac.frame_info(moon_pa_frame)?,
        HarmonicsMem::from_shadr(&jggrx_meta.uri, 80, 80, true)?,
    );

    // Include the spherical harmonics into the orbital dynamics.
    orbital_dyn.accel_models.push(sph_harmonics);

    // We define the solar radiation pressure, using the default solar flux and accounting only
    // for the eclipsing caused by the Earth and Moon.
    // Note that by default, enabling the SolarPressure model will also enable the estimation of the coefficient of reflectivity.
    let srp_dyn = SolarPressure::new(vec![EARTH_J2000, MOON_J2000], almanac.clone())?;

    // Finalize setting up the dynamics, specifying the force models (orbital_dyn) separately from the
    // acceleration models (SRP in this case). Use `from_models` to specify multiple accel models.
    let dynamics = SpacecraftDynamics::from_model(orbital_dyn, srp_dyn);

    println!("{dynamics}");

    // Now we can build the propagator.
    let setup = Propagator::default_dp78(dynamics.clone());

    // For reference, let's build the trajectory with Nyx's models from that LRO state.
    let (sim_final, traj_as_sim) = setup
        .with(*traj_as_flown.first(), almanac.clone())
        .until_epoch_with_traj(traj_as_flown.last().epoch())?;

    println!("SIM INIT:  {:x}", traj_as_flown.first());
    println!("SIM FINAL: {sim_final:x}");
    // Compute RIC difference between SIM and LRO ephem
    let sim_lro_delta = sim_final
        .orbit
        .ric_difference(&traj_as_flown.last().orbit)?;
    println!("{traj_as_sim}");
    println!(
        "SIM v LRO - RIC Position (m): {:.3}",
        sim_lro_delta.radius_km * 1e3
    );
    println!(
        "SIM v LRO - RIC Velocity (m/s): {:.3}",
        sim_lro_delta.velocity_km_s * 1e3
    );

    traj_as_sim.ric_diff_to_parquet(
        &traj_as_flown,
        "./04_lro_sim_truth_error.parquet",
        ExportCfg::default(),
    )?;

    // ==================== //
    // === OD SIMULATOR === //
    // ==================== //

    // After quite some time trying to exactly match the model, we still end up with an oscillatory difference on the order of 150 meters between the propagated state
    // and the truth LRO state.

    // Therefore, we will actually run an estimation from a dispersed LRO state.
    // The sc_seed is the true LRO state from the BSP.
    let sc_seed = *traj_as_flown.first();

    // Load the Deep Space Network ground stations.
    // Nyx allows you to build these at runtime but it's pretty static so we can just load them from YAML.
    let ground_station_file: PathBuf = [
        env!("CARGO_MANIFEST_DIR"),
        "examples",
        "04_lro_od",
        "dsn-network.yaml",
    ]
    .iter()
    .collect();

    let devices = GroundStation::load_named(ground_station_file)?;

    let mut proc_devices = devices.clone();

    // Increase the noise in the devices to accept more measurements.
    for gs in proc_devices.values_mut() {
        if let Some(noise) = &mut gs
            .stochastic_noises
            .as_mut()
            .unwrap()
            .get_mut(&MeasurementType::Range)
        {
            *noise.white_noise.as_mut().unwrap() *= 3.0;
        }
    }

    // Typical OD software requires that you specify your own tracking schedule or you'll have overlapping measurements.
    // Nyx can build a tracking schedule for you based on the first station with access.
    let trkconfg_yaml: PathBuf = [
        env!("CARGO_MANIFEST_DIR"),
        "examples",
        "04_lro_od",
        "tracking-cfg.yaml",
    ]
    .iter()
    .collect();

    let configs: BTreeMap<String, TrkConfig> = TrkConfig::load_named(trkconfg_yaml)?;

    // Build the tracking arc simulation to generate a "standard measurement".
    let mut trk = TrackingArcSim::<Spacecraft, GroundStation>::with_seed(
        devices.clone(),
        traj_as_flown.clone(),
        configs,
        123, // Set a seed for reproducibility
    )?;

    trk.build_schedule(almanac.clone())?;
    let arc = trk.generate_measurements(almanac.clone())?;
    // Save the simulated tracking data
    arc.to_parquet_simple("./04_lro_simulated_tracking.parquet")?;

    // We'll note that in our case, we have continuous coverage of LRO when the vehicle is not behind the Moon.
    println!("{arc}");

    // Now that we have simulated measurements, we'll run the orbit determination.

    // ===================== //
    // === OD ESTIMATION === //
    // ===================== //

    let sc = SpacecraftUncertainty::builder()
        .nominal(sc_seed)
        .frame(LocalFrame::RIC)
        .x_km(0.5)
        .y_km(0.5)
        .z_km(0.5)
        .vx_km_s(5e-3)
        .vy_km_s(5e-3)
        .vz_km_s(5e-3)
        .build();

    // Build the filter initial estimate, which we will reuse in the filter.
    let mut initial_estimate = sc.to_estimate()?;
    initial_estimate.covar *= 3.0;

    println!("== FILTER STATE ==\n{sc_seed:x}\n{initial_estimate}");

    // Build the SNC in the Moon J2000 frame, specified as a velocity noise over time.
    let process_noise = ProcessNoise3D::from_velocity_km_s(
        &[1e-10, 1e-10, 1e-10],
        1 * Unit::Hour,
        10 * Unit::Minute,
        None,
    );

    println!("{process_noise}");

    // We'll set up the OD process to reject measurements whose residuals are move than 3 sigmas away from what we expect.
    let odp = SpacecraftKalmanOD::new(
        setup,
        KalmanVariant::ReferenceUpdate,
        Some(ResidRejectCrit::default()),
        proc_devices,
        almanac.clone(),
    )
    .with_process_noise(process_noise);

    let od_sol = odp.process_arc(initial_estimate, &arc)?;

    let final_est = od_sol.estimates.last().unwrap();

    println!("{final_est}");

    let ric_err = traj_as_flown
        .at(final_est.epoch())?
        .orbit
        .ric_difference(&final_est.orbital_state())?;
    println!("== RIC at end ==");
    println!("RIC Position (m): {:.3}", ric_err.radius_km * 1e3);
    println!("RIC Velocity (m/s): {:.3}", ric_err.velocity_km_s * 1e3);

    println!(
        "Num residuals rejected: #{}",
        od_sol.rejected_residuals().len()
    );
    println!(
        "Percentage within +/-3: {}",
        od_sol.residual_ratio_within_threshold(3.0).unwrap()
    );
    println!("Ratios normal? {}", od_sol.is_normal(None).unwrap());

    od_sol.to_parquet("./04_lro_od_results.parquet", ExportCfg::default())?;

    // In our case, we have the truth trajectory from NASA.
    // So we can compute the RIC state difference between the real LRO ephem and what we've just estimated.
    // Export the OD trajectory first.
    let od_trajectory = od_sol.to_traj()?;
    // Build the RIC difference.
    od_trajectory.ric_diff_to_parquet(
        &traj_as_flown,
        "./04_lro_od_truth_error.parquet",
        ExportCfg::default(),
    )?;

    Ok(())
}

pub fn and_with_process_noise(self, process_noise: ProcessNoise<Accel>) -> Self

Appends the provided process noise to the list the existing process noise configurations.

impl<D: Dynamics, MsrSize: DimName, Accel: DimName, Trk: TrackerSensitivity<D::StateType, D::StateType>> KalmanODProcess<D, MsrSize, Accel, Trk>

where D::StateType: Interpolatable + Add<OVector<f64, <D::StateType as State>::Size>, Output = D::StateType>, <DefaultAllocator as Allocator<<D::StateType as State>::VecLength>>::Buffer<f64>: Send, <DefaultAllocator as Allocator<<D::StateType as State>::Size>>::Buffer<f64>: Copy, <DefaultAllocator as Allocator<<D::StateType as State>::Size, <D::StateType as State>::Size>>::Buffer<f64>: Copy, DefaultAllocator: Allocator<<D::StateType as State>::Size> + Allocator<<D::StateType as State>::VecLength> + Allocator<MsrSize> + Allocator<MsrSize, <D::StateType as State>::Size> + Allocator<<D::StateType as State>::Size, MsrSize> + Allocator<MsrSize, MsrSize> + Allocator<<D::StateType as State>::Size, <D::StateType as State>::Size> + Allocator<Accel> + Allocator<Accel, Accel> + Allocator<<D::StateType as State>::Size, Accel> + Allocator<Accel, <D::StateType as State>::Size>,

pub fn builder() -> KalmanODProcessBuilder<D, MsrSize, Accel, Trk, ((), (), (), (), (), (), (), (), ())>

Create a builder for building KalmanODProcess. On the builder, call .prop(...), .kf_variant(...)(optional), .resid_crit(...)(optional), .devices(...)(optional), .process_noise(...)(optional), .max_step(...)(optional), .epoch_precision(...)(optional), .almanac(...), ._msr_size(...)(optional) to set the values of the fields. Finally, call .build() to create the instance of KalmanODProcess.

impl<D: Dynamics, MsrSize: DimName, Accel: DimName, Trk: TrackerSensitivity<D::StateType, D::StateType>> KalmanODProcess<D, MsrSize, Accel, Trk>

where D::StateType: Interpolatable + Add<OVector<f64, <D::StateType as State>::Size>, Output = D::StateType>, <DefaultAllocator as Allocator<<D::StateType as State>::VecLength>>::Buffer<f64>: Send, <DefaultAllocator as Allocator<<D::StateType as State>::Size>>::Buffer<f64>: Copy, <DefaultAllocator as Allocator<<D::StateType as State>::Size, <D::StateType as State>::Size>>::Buffer<f64>: Copy, DefaultAllocator: Allocator<<D::StateType as State>::Size> + Allocator<<D::StateType as State>::VecLength> + Allocator<MsrSize> + Allocator<MsrSize, <D::StateType as State>::Size> + Allocator<<D::StateType as State>::Size, MsrSize> + Allocator<MsrSize, MsrSize> + Allocator<<D::StateType as State>::Size, <D::StateType as State>::Size> + Allocator<Accel> + Allocator<Accel, Accel> + Allocator<<D::StateType as State>::Size, Accel> + Allocator<Accel, <D::StateType as State>::Size> + Allocator<Const<1>, MsrSize>,

pub fn process_arc( &self, initial_estimate: KfEstimate<D::StateType>, arc: &TrackingDataArc, ) -> Result<ODSolution<D::StateType, KfEstimate<D::StateType>, MsrSize, Trk>, ODError>

Process the provided tracking arc for this orbit determination process.

Examples found in repository

examples/05_cislunar_spacecraft_link_od/main.rs (line 228)

fn main() -> Result<(), Box<dyn Error>> {
    pel::init();

    // ====================== //
    // === ALMANAC SET UP === //
    // ====================== //

    let manifest_dir =
        PathBuf::from(std::env::var("CARGO_MANIFEST_DIR").unwrap_or(".".to_string()));

    let out = manifest_dir.join("data/04_output/");

    let almanac = Arc::new(
        Almanac::new(
            &manifest_dir
                .join("data/01_planetary/pck08.pca")
                .to_string_lossy(),
        )
        .unwrap()
        .load(
            &manifest_dir
                .join("data/01_planetary/de440s.bsp")
                .to_string_lossy(),
        )
        .unwrap(),
    );

    let eme2k = almanac.frame_info(EARTH_J2000).unwrap();
    let moon_iau = almanac.frame_info(IAU_MOON_FRAME).unwrap();

    let epoch = Epoch::from_gregorian_tai(2021, 5, 29, 19, 51, 16, 852_000);
    let nrho = Orbit::cartesian(
        166_473.631_302_239_7,
        -274_715.487_253_382_7,
        -211_233.210_176_686_7,
        0.933_451_604_520_018_4,
        0.436_775_046_841_900_9,
        -0.082_211_021_250_348_95,
        epoch,
        eme2k,
    );

    let tx_nrho_sc = Spacecraft::from(nrho);

    let state_luna = almanac.transform_to(nrho, MOON_J2000, None).unwrap();
    println!("Start state (dynamics: Earth, Moon, Sun gravity):\n{state_luna}");

    let bodies = vec![EARTH, SUN];
    let dynamics = SpacecraftDynamics::new(OrbitalDynamics::point_masses(bodies));

    let setup = Propagator::rk89(
        dynamics,
        IntegratorOptions::builder().max_step(0.5.minutes()).build(),
    );

    /* == Propagate the NRHO vehicle == */
    let prop_time = 1.1 * state_luna.period().unwrap();

    let (nrho_final, mut tx_traj) = setup
        .with(tx_nrho_sc, almanac.clone())
        .for_duration_with_traj(prop_time)
        .unwrap();

    tx_traj.name = Some("NRHO Tx SC".to_string());

    println!("{tx_traj}");

    /* == Propagate an LLO vehicle == */
    let llo_orbit =
        Orbit::try_keplerian_altitude(110.0, 1e-4, 90.0, 0.0, 0.0, 0.0, epoch, moon_iau).unwrap();

    let llo_sc = Spacecraft::builder().orbit(llo_orbit).build();

    let (_, llo_traj) = setup
        .with(llo_sc, almanac.clone())
        .until_epoch_with_traj(nrho_final.epoch())
        .unwrap();

    // Export the subset of the first two hours.
    llo_traj
        .clone()
        .filter_by_offset(..2.hours())
        .to_parquet_simple(out.join("05_caps_llo_truth.pq"), almanac.clone())?;

    /* == Setup the interlink == */

    let mut measurement_types = IndexSet::new();
    measurement_types.insert(MeasurementType::Range);
    measurement_types.insert(MeasurementType::Doppler);

    let mut stochastics = IndexMap::new();

    let sa45_csac_allan_dev = 1e-11;

    stochastics.insert(
        MeasurementType::Range,
        StochasticNoise::from_hardware_range_km(
            sa45_csac_allan_dev,
            10.0.seconds(),
            link_specific::ChipRate::StandardT4B,
            link_specific::SN0::Average,
        ),
    );

    stochastics.insert(
        MeasurementType::Doppler,
        StochasticNoise::from_hardware_doppler_km_s(
            sa45_csac_allan_dev,
            10.0.seconds(),
            link_specific::CarrierFreq::SBand,
            link_specific::CN0::Average,
        ),
    );

    let interlink = InterlinkTxSpacecraft {
        traj: tx_traj,
        measurement_types,
        integration_time: None,
        timestamp_noise_s: None,
        ab_corr: Aberration::LT,
        stochastic_noises: Some(stochastics),
    };

    // Devices are the transmitter, which is our NRHO vehicle.
    let mut devices = BTreeMap::new();
    devices.insert("NRHO Tx SC".to_string(), interlink);

    let mut configs = BTreeMap::new();
    configs.insert(
        "NRHO Tx SC".to_string(),
        TrkConfig::builder()
            .strands(vec![Strand {
                start: epoch,
                end: nrho_final.epoch(),
            }])
            .build(),
    );

    let mut trk_sim =
        TrackingArcSim::with_seed(devices.clone(), llo_traj.clone(), configs, 0).unwrap();
    println!("{trk_sim}");

    let trk_data = trk_sim.generate_measurements(almanac.clone()).unwrap();
    println!("{trk_data}");

    trk_data
        .to_parquet_simple(out.clone().join("nrho_interlink_msr.pq"))
        .unwrap();

    // Run a truth OD where we estimate the LLO position
    let llo_uncertainty = SpacecraftUncertainty::builder()
        .nominal(llo_sc)
        .x_km(1.0)
        .y_km(1.0)
        .z_km(1.0)
        .vx_km_s(1e-3)
        .vy_km_s(1e-3)
        .vz_km_s(1e-3)
        .build();

    let mut proc_devices = devices.clone();

    // Define the initial estimate, randomized, seed for reproducibility
    let mut initial_estimate = llo_uncertainty.to_estimate_randomized(Some(0)).unwrap();
    // Inflate the covariance -- https://github.com/nyx-space/nyx/issues/339
    initial_estimate.covar *= 2.5;

    // Increase the noise in the devices to accept more measurements.

    for link in proc_devices.values_mut() {
        for noise in &mut link.stochastic_noises.as_mut().unwrap().values_mut() {
            *noise.white_noise.as_mut().unwrap() *= 3.0;
        }
    }

    let init_err = initial_estimate
        .orbital_state()
        .ric_difference(&llo_orbit)
        .unwrap();

    println!("initial estimate:\n{initial_estimate}");
    println!("RIC errors = {init_err}",);

    let odp = InterlinkKalmanOD::new(
        setup.clone(),
        KalmanVariant::ReferenceUpdate,
        Some(ResidRejectCrit::default()),
        proc_devices,
        almanac.clone(),
    );

    // Shrink the data to process.
    let arc = trk_data.filter_by_offset(..2.hours());

    let od_sol = odp.process_arc(initial_estimate, &arc).unwrap();

    println!("{od_sol}");

    od_sol
        .to_parquet(
            out.join("05_caps_interlink_od_sol.pq"),
            ExportCfg::default(),
        )
        .unwrap();

    let od_traj = od_sol.to_traj().unwrap();

    od_traj
        .ric_diff_to_parquet(
            &llo_traj,
            out.join("05_caps_interlink_llo_est_error.pq"),
            ExportCfg::default(),
        )
        .unwrap();

    let final_est = od_sol.estimates.last().unwrap();
    assert!(final_est.within_3sigma(), "should be within 3 sigma");

    println!("ESTIMATE\n{final_est:x}\n");
    let truth = llo_traj.at(final_est.epoch()).unwrap();
    println!("TRUTH\n{truth:x}");

    let final_err = truth
        .orbit
        .ric_difference(&final_est.orbital_state())
        .unwrap();
    println!("ERROR {final_err}");

    // Build the residuals versus reference plot.
    let rvr_sol = odp
        .process_arc(initial_estimate, &arc.resid_vs_ref_check())
        .unwrap();

    rvr_sol
        .to_parquet(
            out.join("05_caps_interlink_resid_v_ref.pq"),
            ExportCfg::default(),
        )
        .unwrap();

    let final_rvr = rvr_sol.estimates.last().unwrap();

    println!("RMAG error {:.3} m", final_err.rmag_km() * 1e3);
    println!(
        "Pure prop error {:.3} m",
        final_rvr
            .orbital_state()
            .ric_difference(&final_est.orbital_state())
            .unwrap()
            .rmag_km()
            * 1e3
    );

    Ok(())
}

examples/04_lro_od/main.rs (line 287)

fn main() -> Result<(), Box<dyn Error>> {
    pel::init();

    // ====================== //
    // === ALMANAC SET UP === //
    // ====================== //

    // Dynamics models require planetary constants and ephemerides to be defined.
    // Let's start by grabbing those by using ANISE's MetaAlmanac.

    let data_folder: PathBuf = [env!("CARGO_MANIFEST_DIR"), "examples", "04_lro_od"]
        .iter()
        .collect();

    let meta = data_folder.join("lro-dynamics.dhall");

    // Load this ephem in the general Almanac we're using for this analysis.
    let mut almanac = MetaAlmanac::new(meta.to_string_lossy().as_ref())
        .map_err(Box::new)?
        .process(true)
        .map_err(Box::new)?;

    let mut moon_pc = almanac.planetary_data.get_by_id(MOON)?;
    moon_pc.mu_km3_s2 = 4902.74987;
    almanac.planetary_data.set_by_id(MOON, moon_pc)?;

    let mut earth_pc = almanac.planetary_data.get_by_id(EARTH)?;
    earth_pc.mu_km3_s2 = 398600.436;
    almanac.planetary_data.set_by_id(EARTH, earth_pc)?;

    // Save this new kernel for reuse.
    // In an operational context, this would be part of the "Lock" process, and should not change throughout the mission.
    almanac
        .planetary_data
        .save_as(&data_folder.join("lro-specific.pca"), true)?;

    // Lock the almanac (an Arc is a read only structure).
    let almanac = Arc::new(almanac);

    // Orbit determination requires a Trajectory structure, which can be saved as parquet file.
    // In our case, the trajectory comes from the BSP file, so we need to build a Trajectory from the almanac directly.
    // To query the Almanac, we need to build the LRO frame in the J2000 orientation in our case.
    // Inspecting the LRO BSP in the ANISE GUI shows us that NASA has assigned ID -85 to LRO.
    let lro_frame = Frame::from_ephem_j2000(-85);

    // To build the trajectory we need to provide a spacecraft template.
    let sc_template = Spacecraft::builder()
        .mass(Mass::from_dry_and_prop_masses(1018.0, 900.0)) // Launch masses
        .srp(SRPData {
            // SRP configuration is arbitrary, but we will be estimating it anyway.
            area_m2: 3.9 * 2.7,
            coeff_reflectivity: 0.96,
        })
        .orbit(Orbit::zero(MOON_J2000)) // Setting a zero orbit here because it's just a template
        .build();
    // Now we can build the trajectory from the BSP file.
    // We'll arbitrarily set the tracking arc to 24 hours with a five second time step.
    let traj_as_flown = Traj::from_bsp(
        lro_frame,
        MOON_J2000,
        almanac.clone(),
        sc_template,
        5.seconds(),
        Some(Epoch::from_str("2024-01-01 00:00:00 UTC")?),
        Some(Epoch::from_str("2024-01-02 00:00:00 UTC")?),
        Aberration::LT,
        Some("LRO".to_string()),
    )?;

    println!("{traj_as_flown}");

    // ====================== //
    // === MODEL MATCHING === //
    // ====================== //

    // Set up the spacecraft dynamics.

    // Specify that the orbital dynamics must account for the graviational pull of the Earth and the Sun.
    // The gravity of the Moon will also be accounted for since the spaceraft in a lunar orbit.
    let mut orbital_dyn = OrbitalDynamics::point_masses(vec![EARTH, SUN, JUPITER_BARYCENTER]);

    // We want to include the spherical harmonics, so let's download the gravitational data from the Nyx Cloud.
    // We're using the GRAIL JGGRX model.
    let mut jggrx_meta = MetaFile {
        uri: "http://public-data.nyxspace.com/nyx/models/Luna_jggrx_1500e_sha.tab.gz".to_string(),
        crc32: Some(0x6bcacda8), // Specifying the CRC32 avoids redownloading it if it's cached.
    };
    // And let's download it if we don't have it yet.
    jggrx_meta.process(true)?;

    // Build the spherical harmonics.
    // The harmonics must be computed in the body fixed frame.
    // We're using the long term prediction of the Moon principal axes frame.
    let moon_pa_frame = MOON_PA_FRAME.with_orient(31008);
    let sph_harmonics = Harmonics::from_stor(
        almanac.frame_info(moon_pa_frame)?,
        HarmonicsMem::from_shadr(&jggrx_meta.uri, 80, 80, true)?,
    );

    // Include the spherical harmonics into the orbital dynamics.
    orbital_dyn.accel_models.push(sph_harmonics);

    // We define the solar radiation pressure, using the default solar flux and accounting only
    // for the eclipsing caused by the Earth and Moon.
    // Note that by default, enabling the SolarPressure model will also enable the estimation of the coefficient of reflectivity.
    let srp_dyn = SolarPressure::new(vec![EARTH_J2000, MOON_J2000], almanac.clone())?;

    // Finalize setting up the dynamics, specifying the force models (orbital_dyn) separately from the
    // acceleration models (SRP in this case). Use `from_models` to specify multiple accel models.
    let dynamics = SpacecraftDynamics::from_model(orbital_dyn, srp_dyn);

    println!("{dynamics}");

    // Now we can build the propagator.
    let setup = Propagator::default_dp78(dynamics.clone());

    // For reference, let's build the trajectory with Nyx's models from that LRO state.
    let (sim_final, traj_as_sim) = setup
        .with(*traj_as_flown.first(), almanac.clone())
        .until_epoch_with_traj(traj_as_flown.last().epoch())?;

    println!("SIM INIT:  {:x}", traj_as_flown.first());
    println!("SIM FINAL: {sim_final:x}");
    // Compute RIC difference between SIM and LRO ephem
    let sim_lro_delta = sim_final
        .orbit
        .ric_difference(&traj_as_flown.last().orbit)?;
    println!("{traj_as_sim}");
    println!(
        "SIM v LRO - RIC Position (m): {:.3}",
        sim_lro_delta.radius_km * 1e3
    );
    println!(
        "SIM v LRO - RIC Velocity (m/s): {:.3}",
        sim_lro_delta.velocity_km_s * 1e3
    );

    traj_as_sim.ric_diff_to_parquet(
        &traj_as_flown,
        "./04_lro_sim_truth_error.parquet",
        ExportCfg::default(),
    )?;

    // ==================== //
    // === OD SIMULATOR === //
    // ==================== //

    // After quite some time trying to exactly match the model, we still end up with an oscillatory difference on the order of 150 meters between the propagated state
    // and the truth LRO state.

    // Therefore, we will actually run an estimation from a dispersed LRO state.
    // The sc_seed is the true LRO state from the BSP.
    let sc_seed = *traj_as_flown.first();

    // Load the Deep Space Network ground stations.
    // Nyx allows you to build these at runtime but it's pretty static so we can just load them from YAML.
    let ground_station_file: PathBuf = [
        env!("CARGO_MANIFEST_DIR"),
        "examples",
        "04_lro_od",
        "dsn-network.yaml",
    ]
    .iter()
    .collect();

    let devices = GroundStation::load_named(ground_station_file)?;

    let mut proc_devices = devices.clone();

    // Increase the noise in the devices to accept more measurements.
    for gs in proc_devices.values_mut() {
        if let Some(noise) = &mut gs
            .stochastic_noises
            .as_mut()
            .unwrap()
            .get_mut(&MeasurementType::Range)
        {
            *noise.white_noise.as_mut().unwrap() *= 3.0;
        }
    }

    // Typical OD software requires that you specify your own tracking schedule or you'll have overlapping measurements.
    // Nyx can build a tracking schedule for you based on the first station with access.
    let trkconfg_yaml: PathBuf = [
        env!("CARGO_MANIFEST_DIR"),
        "examples",
        "04_lro_od",
        "tracking-cfg.yaml",
    ]
    .iter()
    .collect();

    let configs: BTreeMap<String, TrkConfig> = TrkConfig::load_named(trkconfg_yaml)?;

    // Build the tracking arc simulation to generate a "standard measurement".
    let mut trk = TrackingArcSim::<Spacecraft, GroundStation>::with_seed(
        devices.clone(),
        traj_as_flown.clone(),
        configs,
        123, // Set a seed for reproducibility
    )?;

    trk.build_schedule(almanac.clone())?;
    let arc = trk.generate_measurements(almanac.clone())?;
    // Save the simulated tracking data
    arc.to_parquet_simple("./04_lro_simulated_tracking.parquet")?;

    // We'll note that in our case, we have continuous coverage of LRO when the vehicle is not behind the Moon.
    println!("{arc}");

    // Now that we have simulated measurements, we'll run the orbit determination.

    // ===================== //
    // === OD ESTIMATION === //
    // ===================== //

    let sc = SpacecraftUncertainty::builder()
        .nominal(sc_seed)
        .frame(LocalFrame::RIC)
        .x_km(0.5)
        .y_km(0.5)
        .z_km(0.5)
        .vx_km_s(5e-3)
        .vy_km_s(5e-3)
        .vz_km_s(5e-3)
        .build();

    // Build the filter initial estimate, which we will reuse in the filter.
    let mut initial_estimate = sc.to_estimate()?;
    initial_estimate.covar *= 3.0;

    println!("== FILTER STATE ==\n{sc_seed:x}\n{initial_estimate}");

    // Build the SNC in the Moon J2000 frame, specified as a velocity noise over time.
    let process_noise = ProcessNoise3D::from_velocity_km_s(
        &[1e-10, 1e-10, 1e-10],
        1 * Unit::Hour,
        10 * Unit::Minute,
        None,
    );

    println!("{process_noise}");

    // We'll set up the OD process to reject measurements whose residuals are move than 3 sigmas away from what we expect.
    let odp = SpacecraftKalmanOD::new(
        setup,
        KalmanVariant::ReferenceUpdate,
        Some(ResidRejectCrit::default()),
        proc_devices,
        almanac.clone(),
    )
    .with_process_noise(process_noise);

    let od_sol = odp.process_arc(initial_estimate, &arc)?;

    let final_est = od_sol.estimates.last().unwrap();

    println!("{final_est}");

    let ric_err = traj_as_flown
        .at(final_est.epoch())?
        .orbit
        .ric_difference(&final_est.orbital_state())?;
    println!("== RIC at end ==");
    println!("RIC Position (m): {:.3}", ric_err.radius_km * 1e3);
    println!("RIC Velocity (m/s): {:.3}", ric_err.velocity_km_s * 1e3);

    println!(
        "Num residuals rejected: #{}",
        od_sol.rejected_residuals().len()
    );
    println!(
        "Percentage within +/-3: {}",
        od_sol.residual_ratio_within_threshold(3.0).unwrap()
    );
    println!("Ratios normal? {}", od_sol.is_normal(None).unwrap());

    od_sol.to_parquet("./04_lro_od_results.parquet", ExportCfg::default())?;

    // In our case, we have the truth trajectory from NASA.
    // So we can compute the RIC state difference between the real LRO ephem and what we've just estimated.
    // Export the OD trajectory first.
    let od_trajectory = od_sol.to_traj()?;
    // Build the RIC difference.
    od_trajectory.ric_diff_to_parquet(
        &traj_as_flown,
        "./04_lro_od_truth_error.parquet",
        ExportCfg::default(),
    )?;

    Ok(())
}

pub fn predict_until( &self, initial_estimate: KfEstimate<D::StateType>, end_epoch: Epoch, ) -> Result<ODSolution<D::StateType, KfEstimate<D::StateType>, MsrSize, Trk>, ODError>

Perform a time update. Continuously predicts the trajectory until the provided end epoch, with covariance mapping at each step.

pub fn predict_for( &self, initial_estimate: KfEstimate<D::StateType>, duration: Duration, ) -> Result<ODSolution<D::StateType, KfEstimate<D::StateType>, MsrSize, Trk>, ODError>

Perform a time update. Continuously predicts the trajectory for the provided duration, with covariance mapping at each step. In other words, this performs a time update.

Examples found in repository

examples/02_jwst_covar_monte_carlo/main.rs (line 127)

fn main() -> Result<(), Box<dyn Error>> {
    pel::init();
    // Dynamics models require planetary constants and ephemerides to be defined.
    // Let's start by grabbing those by using ANISE's latest MetaAlmanac.
    // For details, refer to https://github.com/nyx-space/anise/blob/master/data/latest.dhall.

    // Download the regularly update of the James Webb Space Telescope reconstucted (or definitive) ephemeris.
    // Refer to https://naif.jpl.nasa.gov/pub/naif/JWST/kernels/spk/aareadme.txt for details.
    let mut latest_jwst_ephem = MetaFile {
        uri: "https://naif.jpl.nasa.gov/pub/naif/JWST/kernels/spk/jwst_rec.bsp".to_string(),
        crc32: None,
    };
    latest_jwst_ephem.process(true)?;

    // Load this ephem in the general Almanac we're using for this analysis.
    let almanac = Arc::new(
        MetaAlmanac::latest()
            .map_err(Box::new)?
            .load_from_metafile(latest_jwst_ephem, true)?,
    );

    // By loading this ephemeris file in the ANISE GUI or ANISE CLI, we can find the NAIF ID of the JWST
    // in the BSP. We need this ID in order to query the ephemeris.
    const JWST_NAIF_ID: i32 = -170;
    // Let's build a frame in the J2000 orientation centered on the JWST.
    const JWST_J2000: Frame = Frame::from_ephem_j2000(JWST_NAIF_ID);

    // Since the ephemeris file is updated regularly, we'll just grab the latest state in the ephem.
    let (earliest_epoch, latest_epoch) = almanac.spk_domain(JWST_NAIF_ID)?;
    println!("JWST defined from {earliest_epoch} to {latest_epoch}");
    // Fetch the state, printing it in the Earth J2000 frame.
    let jwst_orbit = almanac.transform(JWST_J2000, EARTH_J2000, latest_epoch, None)?;
    println!("{jwst_orbit:x}");

    // Build the spacecraft
    // SRP area assumed to be the full sunshield and mass if 6200.0 kg, c.f. https://webb.nasa.gov/content/about/faqs/facts.html
    // SRP Coefficient of reflectivity assumed to be that of Kapton, i.e. 2 - 0.44 = 1.56, table 1 from https://amostech.com/TechnicalPapers/2018/Poster/Bengtson.pdf
    let jwst = Spacecraft::builder()
        .orbit(jwst_orbit)
        .srp(SRPData {
            area_m2: 21.197 * 14.162,
            coeff_reflectivity: 1.56,
        })
        .mass(Mass::from_dry_mass(6200.0))
        .build();

    // Build up the spacecraft uncertainty builder.
    // We can use the spacecraft uncertainty structure to build this up.
    // We start by specifying the nominal state (as defined above), then the uncertainty in position and velocity
    // in the RIC frame. We could also specify the Cr, Cd, and mass uncertainties, but these aren't accounted for until
    // Nyx can also estimate the deviation of the spacecraft parameters.
    let jwst_uncertainty = SpacecraftUncertainty::builder()
        .nominal(jwst)
        .frame(LocalFrame::RIC)
        .x_km(0.5)
        .y_km(0.3)
        .z_km(1.5)
        .vx_km_s(1e-4)
        .vy_km_s(0.6e-3)
        .vz_km_s(3e-3)
        .build();

    println!("{jwst_uncertainty}");

    // Build the Kalman filter estimate.
    // Note that we could have used the KfEstimate structure directly (as seen throughout the OD integration tests)
    // but this approach requires quite a bit more boilerplate code.
    let jwst_estimate = jwst_uncertainty.to_estimate()?;

    // Set up the spacecraft dynamics.
    // We'll use the point masses of the Earth, Sun, Jupiter (barycenter, because it's in the DE440), and the Moon.
    // We'll also enable solar radiation pressure since the James Webb has a huge and highly reflective sun shield.

    let orbital_dyn = OrbitalDynamics::point_masses(vec![MOON, SUN, JUPITER_BARYCENTER]);
    let srp_dyn = SolarPressure::new(vec![EARTH_J2000, MOON_J2000], almanac.clone())?;

    // Finalize setting up the dynamics.
    let dynamics = SpacecraftDynamics::from_model(orbital_dyn, srp_dyn);

    // Build the propagator set up to use for the whole analysis.
    let setup = Propagator::default(dynamics);

    // All of the analysis will use this duration.
    let prediction_duration = 6.5 * Unit::Day;

    // === Covariance mapping ===
    // For the covariance mapping / prediction, we'll use the common orbit determination approach.
    // This is done by setting up a spacecraft Kalman filter OD process, and predicting for the analysis duration.

    // Build the propagation instance for the OD process.
    let odp = SpacecraftKalmanOD::new(
        setup.clone(),
        KalmanVariant::DeviationTracking,
        None,
        BTreeMap::new(),
        almanac.clone(),
    );

    // The prediction step is 1 minute by default, configured in the OD process, i.e. how often we want to know the covariance.
    assert_eq!(odp.max_step, 1_i64.minutes());
    // Finally, predict, and export the trajectory with covariance to a parquet file.
    let od_sol = odp.predict_for(jwst_estimate, prediction_duration)?;
    od_sol.to_parquet("./02_jwst_covar_map.parquet", ExportCfg::default())?;

    // === Monte Carlo framework ===
    // Nyx comes with a complete multi-threaded Monte Carlo frame. It's blazing fast.

    let my_mc = MonteCarlo::new(
        jwst, // Nominal state
        jwst_estimate.to_random_variable()?,
        "02_jwst".to_string(), // Scenario name
        None, // No specific seed specified, so one will be drawn from the computer's entropy.
    );

    let num_runs = 5_000;
    let rslts = my_mc.run_until_epoch(
        setup,
        almanac.clone(),
        jwst.epoch() + prediction_duration,
        num_runs,
    );

    assert_eq!(rslts.runs.len(), num_runs);
    // Finally, export these results, computing the eclipse percentage for all of these results.

    // For all of the resulting trajectories, we'll want to compute the percentage of penumbra and umbra.
    let eclipse_loc = EclipseLocator::cislunar(almanac.clone());
    let umbra_event = eclipse_loc.to_umbra_event();
    let penumbra_event = eclipse_loc.to_penumbra_event();

    rslts.to_parquet(
        "02_jwst_monte_carlo.parquet",
        Some(vec![&umbra_event, &penumbra_event]),
        ExportCfg::default(),
        almanac,
    )?;

    Ok(())
}

Trait Implementations

impl<D: Clone + Dynamics, MsrSize: Clone + DimName, Accel: Clone + DimName, Trk: Clone + TrackerSensitivity<D::StateType, D::StateType>> Clone for KalmanODProcess<D, MsrSize, Accel, Trk>

where D::StateType: Interpolatable + Add<OVector<f64, <D::StateType as State>::Size>, Output = D::StateType>, <DefaultAllocator as Allocator<<D::StateType as State>::VecLength>>::Buffer<f64>: Send, <DefaultAllocator as Allocator<<D::StateType as State>::Size>>::Buffer<f64>: Copy, <DefaultAllocator as Allocator<<D::StateType as State>::Size, <D::StateType as State>::Size>>::Buffer<f64>: Copy, DefaultAllocator: Allocator<<D::StateType as State>::Size> + Allocator<<D::StateType as State>::VecLength> + Allocator<MsrSize> + Allocator<MsrSize, <D::StateType as State>::Size> + Allocator<<D::StateType as State>::Size, MsrSize> + Allocator<MsrSize, MsrSize> + Allocator<<D::StateType as State>::Size, <D::StateType as State>::Size> + Allocator<Accel> + Allocator<Accel, Accel> + Allocator<<D::StateType as State>::Size, Accel> + Allocator<Accel, <D::StateType as State>::Size>,

fn clone(&self) -> KalmanODProcess<D, MsrSize, Accel, Trk>

Returns a duplicate of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.