Struct TrackingArc

pub struct TrackingArc<Msr>
where
    Msr: Measurement,
    DefaultAllocator: Allocator<Msr::MeasurementSize>,
{
    pub device_cfg: String,
    pub measurements: Vec<(String, Msr)>,
}

Tracking arc contains the tracking data generated by the tracking devices defined in this structure. This structure is shared between both simulated and real tracking arcs.

Fields

device_cfg: String

The YAML configuration to set up these devices

measurements: Vec<(String, Msr)>

A chronological list of the measurements to the devices used to generate these measurements. If the name of the device does not appear in the list of devices, then the measurement will be ignored.

Implementations

impl<Msr> TrackingArc<Msr>

where Msr: Measurement, DefaultAllocator: Allocator<Msr::MeasurementSize> + Allocator<Msr::MeasurementSize, Msr::MeasurementSize>,

pub fn to_parquet_simple<P: AsRef<Path> + Debug>( &self, path: P, ) -> Result<PathBuf, Box<dyn Error>>

Store this tracking arc to a parquet file.

Examples found in repository

examples/04_lro_od/main.rs (line 226)

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().to_string())
        .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()
        .dry_mass_kg(1018.0) // Launch masses
        .fuel_mass_kg(900.0)
        .srp(SrpConfig {
            // SRP configuration is arbitrary, but we will be estimating it anyway.
            area_m2: 3.9 * 2.7,
            cr: 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 48 hours with a one minute 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 moon_pa_frame = IAU_MOON_FRAME;
    let sph_harmonics = Harmonics::from_stor(
        almanac.frame_from_uid(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_many(ground_station_file)?;

    // 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, RangeDoppler, _>::new(
        devices,
        traj_as_flown.clone(),
        configs,
    )?;

    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 initial_estimate = sc.to_estimate()?;

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

    let kf = KF::new(
        // Increase the initial covariance to account for larger deviation.
        initial_estimate,
        // Until https://github.com/nyx-space/nyx/issues/351, we need to specify the SNC in the acceleration of the Moon J2000 frame.
        SNC3::from_diagonal(10 * Unit::Minute, &[1e-11, 1e-11, 1e-11]),
    );

    // We'll set up the OD process to reject measurements whose residuals are mover than 4 sigmas away from what we expect.
    let mut odp = ODProcess::ckf(
        setup.with(initial_estimate.state().with_stm(), almanac.clone()),
        kf,
        Some(ResidRejectCrit::default()),
        almanac.clone(),
    );

    odp.process_arc::<GroundStation>(&arc)?;

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

    odp.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 = odp.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 to_parquet<P: AsRef<Path> + Debug>( &self, path: P, cfg: ExportCfg, ) -> Result<PathBuf, Box<dyn Error>>

Store this tracking arc to a parquet file, with optional metadata and a timestamp appended to the filename.

pub fn device_names(&self) -> HashSet<&String>

Returns the set of devices from which measurements were taken. This accounts for the availability of measurements, so if a device was not available, it will not appear in this set.

pub fn min_duration_sep(&self) -> Option<Duration>

Returns the minimum duration between two subsequent measurements. This is important to correctly set up the propagator and not miss any measurement.

pub fn rebuild_devices<MsrIn, D>( &self, ) -> Result<BTreeMap<String, D>, ConfigError>

where MsrIn: Interpolatable, D: TrackingDeviceSim<MsrIn, Msr>, DefaultAllocator: Allocator<<MsrIn as State>::Size> + Allocator<<MsrIn as State>::Size, <MsrIn as State>::Size> + Allocator<<MsrIn as State>::VecLength>,

If this tracking arc has devices that can be used to generate simulated measurements, then this function can be used to rebuild said measurement devices

pub fn filter_by_epoch<R: RangeBounds<Epoch>>(&self, bound: R) -> Self

Returns a new tracking arc that only contains measurements that fall within the given epoch range.

pub fn filter_by_offset<R: RangeBounds<Duration>>(&self, bound: R) -> Self

Returns a new tracking arc that only contains measurements that fall within the given offset from the first epoch

pub fn set_devices<MsrIn, D>( &mut self, devices: Vec<D>, configs: BTreeMap<String, TrkConfig>, ) -> Result<(), Box<dyn Error>>

where MsrIn: Interpolatable, D: TrackingDeviceSim<MsrIn, Msr>, DefaultAllocator: Allocator<<MsrIn as State>::Size> + Allocator<<MsrIn as State>::Size, <MsrIn as State>::Size> + Allocator<<MsrIn as State>::VecLength>,

If this tracking arc has devices that can be used to generate simulated measurements, then this function can be used to rebuild said measurement devices

Trait Implementations

impl<Msr> Clone for TrackingArc<Msr>

where Msr: Measurement + Clone, DefaultAllocator: Allocator<Msr::MeasurementSize>,

fn clone(&self) -> TrackingArc<Msr>

Returns a copy of the value.

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

Performs copy-assignment from source.

impl<Msr> Debug for TrackingArc<Msr>

where Msr: Measurement + Debug, DefaultAllocator: Allocator<Msr::MeasurementSize>,

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl<Msr> Default for TrackingArc<Msr>

where Msr: Measurement + Default, DefaultAllocator: Allocator<Msr::MeasurementSize>,

fn default() -> TrackingArc<Msr>

Returns the “default value” for a type.

impl<Msr> Display for TrackingArc<Msr>

where Msr: Measurement, DefaultAllocator: Allocator<Msr::MeasurementSize> + Allocator<Msr::MeasurementSize, Msr::MeasurementSize>,

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.