pub struct KF<T, A, M>where
A: DimName,
M: DimName,
T: State,
DefaultAllocator: Allocator<M> + Allocator<<T as State>::Size> + Allocator<<T as State>::VecLength> + Allocator<A> + Allocator<M, M> + Allocator<M, <T as State>::Size> + Allocator<<T as State>::Size, <T as State>::Size> + Allocator<A, A> + Allocator<<T as State>::Size, A> + Allocator<A, <T as State>::Size>,
<DefaultAllocator as Allocator<<T as State>::Size>>::Buffer<f64>: Copy,
<DefaultAllocator as Allocator<<T as State>::Size, <T as State>::Size>>::Buffer<f64>: Copy,{
pub prev_estimate: KfEstimate<T>,
pub process_noise: Vec<SNC<A>>,
pub ekf: bool,
/* private fields */
}
Expand description
Defines both a Classical and an Extended Kalman filter (CKF and EKF) T: Type of state A: Acceleration size (for SNC) M: Measurement size (used for the sensitivity matrix)
Fields§
§prev_estimate: KfEstimate<T>
The previous estimate used in the KF computations.
process_noise: Vec<SNC<A>>
A sets of process noise (usually noted Q), must be ordered chronologically
ekf: bool
Determines whether this KF should operate as a Conventional/Classical Kalman filter or an Extended Kalman Filter. Recall that one should switch to an Extended KF only once the estimate is good (i.e. after a few good measurement updates on a CKF).
Implementations§
Source§impl<T, A, M> KF<T, A, M>where
A: DimName,
M: DimName,
T: State,
DefaultAllocator: Allocator<M> + Allocator<<T as State>::Size> + Allocator<<T as State>::VecLength> + Allocator<A> + Allocator<M, M> + Allocator<M, <T as State>::Size> + Allocator<<T as State>::Size, M> + Allocator<<T as State>::Size, <T as State>::Size> + Allocator<A, A> + Allocator<<T as State>::Size, A> + Allocator<A, <T as State>::Size>,
<DefaultAllocator as Allocator<<T as State>::Size>>::Buffer<f64>: Copy,
<DefaultAllocator as Allocator<<T as State>::Size, <T as State>::Size>>::Buffer<f64>: Copy,
impl<T, A, M> KF<T, A, M>where
A: DimName,
M: DimName,
T: State,
DefaultAllocator: Allocator<M> + Allocator<<T as State>::Size> + Allocator<<T as State>::VecLength> + Allocator<A> + Allocator<M, M> + Allocator<M, <T as State>::Size> + Allocator<<T as State>::Size, M> + Allocator<<T as State>::Size, <T as State>::Size> + Allocator<A, A> + Allocator<<T as State>::Size, A> + Allocator<A, <T as State>::Size>,
<DefaultAllocator as Allocator<<T as State>::Size>>::Buffer<f64>: Copy,
<DefaultAllocator as Allocator<<T as State>::Size, <T as State>::Size>>::Buffer<f64>: Copy,
Sourcepub fn new(initial_estimate: KfEstimate<T>, process_noise: SNC<A>) -> Self
pub fn new(initial_estimate: KfEstimate<T>, process_noise: SNC<A>) -> Self
Initializes this KF with an initial estimate, measurement noise, and one process noise
Examples found in repository?
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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(())
}
Sourcepub fn with_sncs(
initial_estimate: KfEstimate<T>,
process_noises: Vec<SNC<A>>,
) -> Self
pub fn with_sncs( initial_estimate: KfEstimate<T>, process_noises: Vec<SNC<A>>, ) -> Self
Initializes this KF with an initial estimate, measurement noise, and several process noise WARNING: SNCs MUST be ordered chronologically! They will be selected automatically by walking the list of SNCs backward until one can be applied!
Source§impl<T, M> KF<T, U3, M>where
M: DimName,
T: State,
DefaultAllocator: Allocator<M> + Allocator<<T as State>::Size> + Allocator<<T as State>::VecLength> + Allocator<M, M> + Allocator<M, <T as State>::Size> + Allocator<<T as State>::Size, M> + Allocator<<T as State>::Size, <T as State>::Size> + Allocator<U3, U3> + Allocator<<T as State>::Size, U3> + Allocator<U3, <T as State>::Size>,
<DefaultAllocator as Allocator<<T as State>::Size>>::Buffer<f64>: Copy,
<DefaultAllocator as Allocator<<T as State>::Size, <T as State>::Size>>::Buffer<f64>: Copy,
impl<T, M> KF<T, U3, M>where
M: DimName,
T: State,
DefaultAllocator: Allocator<M> + Allocator<<T as State>::Size> + Allocator<<T as State>::VecLength> + Allocator<M, M> + Allocator<M, <T as State>::Size> + Allocator<<T as State>::Size, M> + Allocator<<T as State>::Size, <T as State>::Size> + Allocator<U3, U3> + Allocator<<T as State>::Size, U3> + Allocator<U3, <T as State>::Size>,
<DefaultAllocator as Allocator<<T as State>::Size>>::Buffer<f64>: Copy,
<DefaultAllocator as Allocator<<T as State>::Size, <T as State>::Size>>::Buffer<f64>: Copy,
Sourcepub fn no_snc(initial_estimate: KfEstimate<T>) -> Self
pub fn no_snc(initial_estimate: KfEstimate<T>) -> Self
Initializes this KF without SNC
Examples found in repository?
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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(SrpConfig {
area_m2: 21.197 * 14.162,
cr: 1.56,
})
.dry_mass_kg(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 OD process, and predicting for the analysis duration.
let ckf = KF::no_snc(jwst_estimate);
// Build the propagation instance for the OD process.
let prop = setup.with(jwst.with_stm(), almanac.clone());
let mut odp = SpacecraftODProcess::ckf(prop, ckf, None, almanac.clone());
// Define the prediction step, i.e. how often we want to know the covariance.
let step = 1_i64.minutes();
// Finally, predict, and export the trajectory with covariance to a parquet file.
odp.predict_for(step, prediction_duration)?;
odp.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§
Source§impl<T, A, M> Clone for KF<T, A, M>where
A: DimName + Clone,
M: DimName + Clone,
T: State + Clone,
DefaultAllocator: Allocator<M> + Allocator<<T as State>::Size> + Allocator<<T as State>::VecLength> + Allocator<A> + Allocator<M, M> + Allocator<M, <T as State>::Size> + Allocator<<T as State>::Size, <T as State>::Size> + Allocator<A, A> + Allocator<<T as State>::Size, A> + Allocator<A, <T as State>::Size>,
<DefaultAllocator as Allocator<<T as State>::Size>>::Buffer<f64>: Copy,
<DefaultAllocator as Allocator<<T as State>::Size, <T as State>::Size>>::Buffer<f64>: Copy,
impl<T, A, M> Clone for KF<T, A, M>where
A: DimName + Clone,
M: DimName + Clone,
T: State + Clone,
DefaultAllocator: Allocator<M> + Allocator<<T as State>::Size> + Allocator<<T as State>::VecLength> + Allocator<A> + Allocator<M, M> + Allocator<M, <T as State>::Size> + Allocator<<T as State>::Size, <T as State>::Size> + Allocator<A, A> + Allocator<<T as State>::Size, A> + Allocator<A, <T as State>::Size>,
<DefaultAllocator as Allocator<<T as State>::Size>>::Buffer<f64>: Copy,
<DefaultAllocator as Allocator<<T as State>::Size, <T as State>::Size>>::Buffer<f64>: Copy,
Source§impl<T, A, M> Debug for KF<T, A, M>where
A: DimName + Debug,
M: DimName + Debug,
T: State + Debug,
DefaultAllocator: Allocator<M> + Allocator<<T as State>::Size> + Allocator<<T as State>::VecLength> + Allocator<A> + Allocator<M, M> + Allocator<M, <T as State>::Size> + Allocator<<T as State>::Size, <T as State>::Size> + Allocator<A, A> + Allocator<<T as State>::Size, A> + Allocator<A, <T as State>::Size>,
<DefaultAllocator as Allocator<<T as State>::Size>>::Buffer<f64>: Copy,
<DefaultAllocator as Allocator<<T as State>::Size, <T as State>::Size>>::Buffer<f64>: Copy,
impl<T, A, M> Debug for KF<T, A, M>where
A: DimName + Debug,
M: DimName + Debug,
T: State + Debug,
DefaultAllocator: Allocator<M> + Allocator<<T as State>::Size> + Allocator<<T as State>::VecLength> + Allocator<A> + Allocator<M, M> + Allocator<M, <T as State>::Size> + Allocator<<T as State>::Size, <T as State>::Size> + Allocator<A, A> + Allocator<<T as State>::Size, A> + Allocator<A, <T as State>::Size>,
<DefaultAllocator as Allocator<<T as State>::Size>>::Buffer<f64>: Copy,
<DefaultAllocator as Allocator<<T as State>::Size, <T as State>::Size>>::Buffer<f64>: Copy,
Source§impl<T, A, M> Filter<T, A, M> for KF<T, A, M>where
A: DimName,
M: DimName,
T: State,
DefaultAllocator: Allocator<M> + Allocator<<T as State>::Size> + Allocator<<T as State>::VecLength> + Allocator<A> + Allocator<M, M> + Allocator<M, <T as State>::Size> + Allocator<<T as State>::Size, M> + Allocator<<T as State>::Size, <T as State>::Size> + Allocator<A, A> + Allocator<<T as State>::Size, A> + Allocator<A, <T as State>::Size> + Allocator<Const<1>, M>,
<DefaultAllocator as Allocator<<T as State>::Size>>::Buffer<f64>: Copy,
<DefaultAllocator as Allocator<<T as State>::Size, <T as State>::Size>>::Buffer<f64>: Copy,
impl<T, A, M> Filter<T, A, M> for KF<T, A, M>where
A: DimName,
M: DimName,
T: State,
DefaultAllocator: Allocator<M> + Allocator<<T as State>::Size> + Allocator<<T as State>::VecLength> + Allocator<A> + Allocator<M, M> + Allocator<M, <T as State>::Size> + Allocator<<T as State>::Size, M> + Allocator<<T as State>::Size, <T as State>::Size> + Allocator<A, A> + Allocator<<T as State>::Size, A> + Allocator<A, <T as State>::Size> + Allocator<Const<1>, M>,
<DefaultAllocator as Allocator<<T as State>::Size>>::Buffer<f64>: Copy,
<DefaultAllocator as Allocator<<T as State>::Size, <T as State>::Size>>::Buffer<f64>: Copy,
Source§fn previous_estimate(&self) -> &Self::Estimate
fn previous_estimate(&self) -> &Self::Estimate
Returns the previous estimate
Source§fn update_h_tilde(&mut self, h_tilde: OMatrix<f64, M, <T as State>::Size>)
fn update_h_tilde(&mut self, h_tilde: OMatrix<f64, M, <T as State>::Size>)
Update the sensitivity matrix (or “H tilde”). This function must be called prior to each
call to measurement_update
.
Source§fn time_update(&mut self, nominal_state: T) -> Result<Self::Estimate, ODError>
fn time_update(&mut self, nominal_state: T) -> Result<Self::Estimate, ODError>
Computes a time update/prediction (i.e. advances the filter estimate with the updated STM).
May return a FilterError if the STM was not updated.
Source§fn measurement_update(
&mut self,
nominal_state: T,
real_obs: &OVector<f64, M>,
computed_obs: &OVector<f64, M>,
measurement_covar: OMatrix<f64, M, M>,
resid_rejection: Option<ResidRejectCrit>,
) -> Result<(Self::Estimate, Residual<M>), ODError>
fn measurement_update( &mut self, nominal_state: T, real_obs: &OVector<f64, M>, computed_obs: &OVector<f64, M>, measurement_covar: OMatrix<f64, M, M>, resid_rejection: Option<ResidRejectCrit>, ) -> Result<(Self::Estimate, Residual<M>), ODError>
Computes the measurement update with a provided real observation and computed observation.
May return a FilterError if the STM or sensitivity matrices were not updated.
Source§fn set_process_noise(&mut self, snc: SNC<A>)
fn set_process_noise(&mut self, snc: SNC<A>)
Overwrites all of the process noises to the one provided
type Estimate = KfEstimate<T>
Source§fn set_previous_estimate(&mut self, est: &Self::Estimate)
fn set_previous_estimate(&mut self, est: &Self::Estimate)
Source§fn is_extended(&self) -> bool
fn is_extended(&self) -> bool
Source§fn set_extended(&mut self, status: bool)
fn set_extended(&mut self, status: bool)
Auto Trait Implementations§
impl<T, A, M> !Freeze for KF<T, A, M>
impl<T, A, M> !RefUnwindSafe for KF<T, A, M>
impl<T, A, M> !Send for KF<T, A, M>
impl<T, A, M> !Sync for KF<T, A, M>
impl<T, A, M> !Unpin for KF<T, A, M>
impl<T, A, M> !UnwindSafe for KF<T, A, M>
Blanket Implementations§
Source§impl<T> BorrowMut<T> for Twhere
T: ?Sized,
impl<T> BorrowMut<T> for Twhere
T: ?Sized,
Source§fn borrow_mut(&mut self) -> &mut T
fn borrow_mut(&mut self) -> &mut T
Source§impl<T> CloneToUninit for Twhere
T: Clone,
impl<T> CloneToUninit for Twhere
T: Clone,
§impl<T> Instrument for T
impl<T> Instrument for T
§fn instrument(self, span: Span) -> Instrumented<Self>
fn instrument(self, span: Span) -> Instrumented<Self>
§fn in_current_span(self) -> Instrumented<Self>
fn in_current_span(self) -> Instrumented<Self>
Source§impl<T> IntoEither for T
impl<T> IntoEither for T
Source§fn into_either(self, into_left: bool) -> Either<Self, Self>
fn into_either(self, into_left: bool) -> Either<Self, Self>
self
into a Left
variant of Either<Self, Self>
if into_left
is true
.
Converts self
into a Right
variant of Either<Self, Self>
otherwise. Read moreSource§fn into_either_with<F>(self, into_left: F) -> Either<Self, Self>
fn into_either_with<F>(self, into_left: F) -> Either<Self, Self>
self
into a Left
variant of Either<Self, Self>
if into_left(&self)
returns true
.
Converts self
into a Right
variant of Either<Self, Self>
otherwise. Read more§impl<T> Pointable for T
impl<T> Pointable for T
§impl<SS, SP> SupersetOf<SS> for SPwhere
SS: SubsetOf<SP>,
impl<SS, SP> SupersetOf<SS> for SPwhere
SS: SubsetOf<SP>,
§fn to_subset(&self) -> Option<SS>
fn to_subset(&self) -> Option<SS>
self
from the equivalent element of its
superset. Read more§fn is_in_subset(&self) -> bool
fn is_in_subset(&self) -> bool
self
is actually part of its subset T
(and can be converted to it).§fn to_subset_unchecked(&self) -> SS
fn to_subset_unchecked(&self) -> SS
self.to_subset
but without any property checks. Always succeeds.§fn from_subset(element: &SS) -> SP
fn from_subset(element: &SS) -> SP
self
to the equivalent element of its superset.