pub trait Estimate<T: State>where
Self: Clone + PartialEq + Sized + Display,
DefaultAllocator: Allocator<<T as State>::Size> + Allocator<<T as State>::Size, <T as State>::Size> + Allocator<<T as State>::VecLength>,{
Show 14 methods
// Required methods
fn zeros(state: T) -> Self;
fn state_deviation(&self) -> OVector<f64, <T as State>::Size>;
fn nominal_state(&self) -> T;
fn covar(&self) -> OMatrix<f64, <T as State>::Size, <T as State>::Size>;
fn predicted_covar(
&self,
) -> OMatrix<f64, <T as State>::Size, <T as State>::Size>;
fn set_state_deviation(
&mut self,
new_state: OVector<f64, <T as State>::Size>,
);
fn set_covar(
&mut self,
new_covar: OMatrix<f64, <T as State>::Size, <T as State>::Size>,
);
fn predicted(&self) -> bool;
fn stm(&self) -> &OMatrix<f64, <T as State>::Size, <T as State>::Size>;
// Provided methods
fn epoch(&self) -> Epoch { ... }
fn set_epoch(&mut self, dt: Epoch) { ... }
fn state(&self) -> T { ... }
fn within_sigma(&self, sigma: f64) -> bool { ... }
fn within_3sigma(&self) -> bool { ... }
}
Expand description
Stores an Estimate, as the result of a time_update
or measurement_update
.
Required Methods§
Sourcefn zeros(state: T) -> Self
fn zeros(state: T) -> Self
An empty estimate. This is useful if wanting to store an estimate outside the scope of a filtering loop.
Sourcefn state_deviation(&self) -> OVector<f64, <T as State>::Size>
fn state_deviation(&self) -> OVector<f64, <T as State>::Size>
The state deviation as computed by the filter.
Sourcefn nominal_state(&self) -> T
fn nominal_state(&self) -> T
The nominal state as reported by the filter dynamics
Sourcefn covar(&self) -> OMatrix<f64, <T as State>::Size, <T as State>::Size>
fn covar(&self) -> OMatrix<f64, <T as State>::Size, <T as State>::Size>
The Covariance of this estimate. Will return the predicted covariance if this is a time update/prediction.
Sourcefn predicted_covar(
&self,
) -> OMatrix<f64, <T as State>::Size, <T as State>::Size>
fn predicted_covar( &self, ) -> OMatrix<f64, <T as State>::Size, <T as State>::Size>
The predicted covariance of this estimate from the time update
Sourcefn set_state_deviation(&mut self, new_state: OVector<f64, <T as State>::Size>)
fn set_state_deviation(&mut self, new_state: OVector<f64, <T as State>::Size>)
Sets the state deviation.
Sourcefn set_covar(
&mut self,
new_covar: OMatrix<f64, <T as State>::Size, <T as State>::Size>,
)
fn set_covar( &mut self, new_covar: OMatrix<f64, <T as State>::Size, <T as State>::Size>, )
Sets the Covariance of this estimate
Provided Methods§
Sourcefn epoch(&self) -> Epoch
fn epoch(&self) -> Epoch
Epoch of this Estimate
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(())
}
fn set_epoch(&mut self, dt: Epoch)
Sourcefn state(&self) -> T
fn state(&self) -> T
The estimated state
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(())
}
Sourcefn within_sigma(&self, sigma: f64) -> bool
fn within_sigma(&self, sigma: f64) -> bool
Returns whether this estimate is within some bound The 68-95-99.7 rule is a good way to assess whether the filter is operating normally
Sourcefn within_3sigma(&self) -> bool
fn within_3sigma(&self) -> bool
Returns whether this estimate is within 3 sigma, which represent 99.7% for a Normal distribution
Dyn Compatibility§
This trait is not dyn compatible.
In older versions of Rust, dyn compatibility was called "object safety", so this trait is not object safe.