pub struct TrackingDataArc {
pub measurements: BTreeMap<Epoch, Measurement>,
pub source: Option<String>,
pub moduli: Option<IndexMap<MeasurementType, f64>>,
pub force_reject: bool,
}
Expand description
Tracking data storing all of measurements as a B-Tree. It inherently does NOT support multiple concurrent measurements from several trackers.
§Measurement Moduli, e.g. range modulus
In the case of ranging, and possibly other data types, a code is used to measure the range to the spacecraft. The length of this code determines the ambiguity resolution, as per equation 9 in section 2.2.2.2 of the JPL DESCANSO, document 214, Pseudo-Noise and Regenerative Ranging. For example, using the JPL Range Code and a frequency range clock of 1 MHz, the range ambiguity is 75,660 km. In other words, as soon as the spacecraft is at a range of 75,660 + 1 km the JPL Range Code will report the vehicle to be at a range of 1 km. This is simply because the range code overlaps with itself, effectively loosing track of its own reference: it’s due to the phase shift of the signal “lapping” the original signal length.
(Spacecraft)
^
| Actual Distance = 75,661 km
|
0 km 75,660 km (Wrap-Around)
|-----------------------------------------------|
When the "code length" is exceeded,
measurements wrap back to 0.
So effectively:
Observed code range = Actual range (mod 75,660 km)
75,661 km → 1 km
Nyx can only resolve the range ambiguity if the tracking data specifies a modulus for this specific measurement type. For example, in the case of the JPL Range Code and a 1 MHz range clock, the ambiguity interval is 75,660 km.
The measurement used in the Orbit Determination Process then becomes the following, where //
represents the Euclidian division.
k = computed_obs // ambiguity_interval
real_obs = measured_obs + k * modulus
Reference: JPL DESCANSO, document 214, Pseudo-Noise and Regenerative Ranging.
Fields§
§measurements: BTreeMap<Epoch, Measurement>
All measurements in this data arc
source: Option<String>
Source file if loaded from a file or saved to a file.
moduli: Option<IndexMap<MeasurementType, f64>>
Optionally provide a map of modulos (e.g. the RANGE_MODULO of CCSDS TDM).
force_reject: bool
Reject all of the measurements, useful for debugging passes.
Implementations§
Source§impl TrackingDataArc
impl TrackingDataArc
Sourcepub fn from_tdm<P: AsRef<Path>>(
path: P,
aliases: Option<HashMap<String, String>>,
) -> Result<Self, InputOutputError>
pub fn from_tdm<P: AsRef<Path>>( path: P, aliases: Option<HashMap<String, String>>, ) -> Result<Self, InputOutputError>
Loads a tracking arc from its serialization in CCSDS TDM.
§Support level
- Only the KVN format is supported.
- Support is limited to orbit determination in “xGEO”, i.e. cislunar and deep space missions.
- Only one metadata and data section per file is tested.
§Data types
Fully supported: - RANGE - DOPPLER_INSTANTANEOUS, DOPPLER_INTEGRATED - ANGLE_1 / ANGLE_2, as azimuth/elevation only
Partially supported: - TRANSMIT_FREQ / RECEIVE_FREQ : these will be converted to Doppler measurements using the TURNAROUND_NUMERATOR and TURNAROUND_DENOMINATOR in the TDM. The freq rate is not supported.
§Metadata support
§Mode
Only the MODE = SEQUENTIAL is supported.
§Time systems / time scales
All timescales supported by hifitime are supported here. This includes: UTC, TAI, GPS, TT, TDB, TAI, GST, QZSST.
§Path
Only one way or two way data is supported, i.e. path must be either PATH n,m,n
or PATH n,m
.
Note that the actual indexes of the path are ignored.
§Participants
PARTICIPANT_1
must be the ground station / tracker.
The second participant is ignored: the user must ensure that the Orbit Determination Process is properly configured and the proper arc is given.
§Turnaround ratio
The turnaround ratio is only accounted for when the data contains RECEIVE_FREQ and TRANSMIT_FREQ data.
§Range and modulus
Only kilometers are supported in range units. Range modulus is accounted for to compute range ambiguity.
Source§impl TrackingDataArc
impl TrackingDataArc
Sourcepub fn from_parquet<P: AsRef<Path>>(path: P) -> Result<Self, InputOutputError>
pub fn from_parquet<P: AsRef<Path>>(path: P) -> Result<Self, InputOutputError>
Loads a tracking arc from its serialization in parquet.
Warning: no metadata is read from the parquet file, even that written to it by Nyx.
Sourcepub fn to_parquet_simple<P: AsRef<Path>>(
&self,
path: P,
) -> Result<PathBuf, Box<dyn Error>>
pub fn to_parquet_simple<P: AsRef<Path>>( &self, path: P, ) -> Result<PathBuf, Box<dyn Error>>
Store this tracking arc to a parquet file.
Examples found in repository?
34fn main() -> Result<(), Box<dyn Error>> {
35 pel::init();
36
37 // ====================== //
38 // === ALMANAC SET UP === //
39 // ====================== //
40
41 let manifest_dir =
42 PathBuf::from(std::env::var("CARGO_MANIFEST_DIR").unwrap_or(".".to_string()));
43
44 let out = manifest_dir.join("data/04_output/");
45
46 let almanac = Arc::new(
47 Almanac::new(
48 &manifest_dir
49 .join("data/01_planetary/pck08.pca")
50 .to_string_lossy(),
51 )
52 .unwrap()
53 .load(
54 &manifest_dir
55 .join("data/01_planetary/de440s.bsp")
56 .to_string_lossy(),
57 )
58 .unwrap(),
59 );
60
61 let eme2k = almanac.frame_from_uid(EARTH_J2000).unwrap();
62 let moon_iau = almanac.frame_from_uid(IAU_MOON_FRAME).unwrap();
63
64 let epoch = Epoch::from_gregorian_tai(2021, 5, 29, 19, 51, 16, 852_000);
65 let nrho = Orbit::cartesian(
66 166_473.631_302_239_7,
67 -274_715.487_253_382_7,
68 -211_233.210_176_686_7,
69 0.933_451_604_520_018_4,
70 0.436_775_046_841_900_9,
71 -0.082_211_021_250_348_95,
72 epoch,
73 eme2k,
74 );
75
76 let tx_nrho_sc = Spacecraft::from(nrho);
77
78 let state_luna = almanac.transform_to(nrho, MOON_J2000, None).unwrap();
79 println!("Start state (dynamics: Earth, Moon, Sun gravity):\n{state_luna}");
80
81 let bodies = vec![EARTH, SUN];
82 let dynamics = SpacecraftDynamics::new(OrbitalDynamics::point_masses(bodies));
83
84 let setup = Propagator::rk89(
85 dynamics,
86 IntegratorOptions::builder().max_step(0.5.minutes()).build(),
87 );
88
89 /* == Propagate the NRHO vehicle == */
90 let prop_time = 1.1 * state_luna.period().unwrap();
91
92 let (nrho_final, mut tx_traj) = setup
93 .with(tx_nrho_sc, almanac.clone())
94 .for_duration_with_traj(prop_time)
95 .unwrap();
96
97 tx_traj.name = Some("NRHO Tx SC".to_string());
98
99 println!("{tx_traj}");
100
101 /* == Propagate an LLO vehicle == */
102 let llo_orbit =
103 Orbit::try_keplerian_altitude(110.0, 1e-4, 90.0, 0.0, 0.0, 0.0, epoch, moon_iau).unwrap();
104
105 let llo_sc = Spacecraft::builder().orbit(llo_orbit).build();
106
107 let (_, llo_traj) = setup
108 .with(llo_sc, almanac.clone())
109 .until_epoch_with_traj(nrho_final.epoch())
110 .unwrap();
111
112 // Export the subset of the first two hours.
113 llo_traj
114 .clone()
115 .filter_by_offset(..2.hours())
116 .to_parquet_simple(out.join("05_caps_llo_truth.pq"), almanac.clone())?;
117
118 /* == Setup the interlink == */
119
120 let mut measurement_types = IndexSet::new();
121 measurement_types.insert(MeasurementType::Range);
122 measurement_types.insert(MeasurementType::Doppler);
123
124 let mut stochastics = IndexMap::new();
125
126 let sa45_csac_allan_dev = 1e-11;
127
128 stochastics.insert(
129 MeasurementType::Range,
130 StochasticNoise::from_hardware_range_km(
131 sa45_csac_allan_dev,
132 10.0.seconds(),
133 link_specific::ChipRate::StandardT4B,
134 link_specific::SN0::Average,
135 ),
136 );
137
138 stochastics.insert(
139 MeasurementType::Doppler,
140 StochasticNoise::from_hardware_doppler_km_s(
141 sa45_csac_allan_dev,
142 10.0.seconds(),
143 link_specific::CarrierFreq::SBand,
144 link_specific::CN0::Average,
145 ),
146 );
147
148 let interlink = InterlinkTxSpacecraft {
149 traj: tx_traj,
150 measurement_types,
151 integration_time: None,
152 timestamp_noise_s: None,
153 ab_corr: Aberration::LT,
154 stochastic_noises: Some(stochastics),
155 };
156
157 // Devices are the transmitter, which is our NRHO vehicle.
158 let mut devices = BTreeMap::new();
159 devices.insert("NRHO Tx SC".to_string(), interlink);
160
161 let mut configs = BTreeMap::new();
162 configs.insert(
163 "NRHO Tx SC".to_string(),
164 TrkConfig::builder()
165 .strands(vec![Strand {
166 start: epoch,
167 end: nrho_final.epoch(),
168 }])
169 .build(),
170 );
171
172 let mut trk_sim =
173 TrackingArcSim::with_seed(devices.clone(), llo_traj.clone(), configs, 0).unwrap();
174 println!("{trk_sim}");
175
176 let trk_data = trk_sim.generate_measurements(almanac.clone()).unwrap();
177 println!("{trk_data}");
178
179 trk_data
180 .to_parquet_simple(out.clone().join("nrho_interlink_msr.pq"))
181 .unwrap();
182
183 // Run a truth OD where we estimate the LLO position
184 let llo_uncertainty = SpacecraftUncertainty::builder()
185 .nominal(llo_sc)
186 .x_km(1.0)
187 .y_km(1.0)
188 .z_km(1.0)
189 .vx_km_s(1e-3)
190 .vy_km_s(1e-3)
191 .vz_km_s(1e-3)
192 .build();
193
194 let mut proc_devices = devices.clone();
195
196 // Define the initial estimate, randomized, seed for reproducibility
197 let mut initial_estimate = llo_uncertainty.to_estimate_randomized(Some(0)).unwrap();
198 // Inflate the covariance -- https://github.com/nyx-space/nyx/issues/339
199 initial_estimate.covar *= 2.5;
200
201 // Increase the noise in the devices to accept more measurements.
202
203 for link in proc_devices.values_mut() {
204 for noise in &mut link.stochastic_noises.as_mut().unwrap().values_mut() {
205 *noise.white_noise.as_mut().unwrap() *= 3.0;
206 }
207 }
208
209 let init_err = initial_estimate
210 .orbital_state()
211 .ric_difference(&llo_orbit)
212 .unwrap();
213
214 println!("initial estimate:\n{initial_estimate}");
215 println!("RIC errors = {init_err}",);
216
217 let odp = InterlinkKalmanOD::new(
218 setup.clone(),
219 KalmanVariant::ReferenceUpdate,
220 Some(ResidRejectCrit::default()),
221 proc_devices,
222 almanac.clone(),
223 );
224
225 // Shrink the data to process.
226 let arc = trk_data.filter_by_offset(..2.hours());
227
228 let od_sol = odp.process_arc(initial_estimate, &arc).unwrap();
229
230 println!("{od_sol}");
231
232 od_sol
233 .to_parquet(
234 out.join(format!("05_caps_interlink_od_sol.pq")),
235 ExportCfg::default(),
236 )
237 .unwrap();
238
239 let od_traj = od_sol.to_traj().unwrap();
240
241 od_traj
242 .ric_diff_to_parquet(
243 &llo_traj,
244 out.join(format!("05_caps_interlink_llo_est_error.pq")),
245 ExportCfg::default(),
246 )
247 .unwrap();
248
249 let final_est = od_sol.estimates.last().unwrap();
250 assert!(final_est.within_3sigma(), "should be within 3 sigma");
251
252 println!("ESTIMATE\n{final_est:x}\n");
253 let truth = llo_traj.at(final_est.epoch()).unwrap();
254 println!("TRUTH\n{truth:x}");
255
256 let final_err = truth
257 .orbit
258 .ric_difference(&final_est.orbital_state())
259 .unwrap();
260 println!("ERROR {final_err}");
261
262 // Build the residuals versus reference plot.
263 let rvr_sol = odp
264 .process_arc(initial_estimate, &arc.resid_vs_ref_check())
265 .unwrap();
266
267 rvr_sol
268 .to_parquet(
269 out.join(format!("05_caps_interlink_resid_v_ref.pq")),
270 ExportCfg::default(),
271 )
272 .unwrap();
273
274 let final_rvr = rvr_sol.estimates.last().unwrap();
275
276 println!("RMAG error {:.3} m", final_err.rmag_km() * 1e3);
277 println!(
278 "Pure prop error {:.3} m",
279 final_rvr
280 .orbital_state()
281 .ric_difference(&final_est.orbital_state())
282 .unwrap()
283 .rmag_km()
284 * 1e3
285 );
286
287 Ok(())
288}
More examples
34fn main() -> Result<(), Box<dyn Error>> {
35 pel::init();
36
37 // ====================== //
38 // === ALMANAC SET UP === //
39 // ====================== //
40
41 // Dynamics models require planetary constants and ephemerides to be defined.
42 // Let's start by grabbing those by using ANISE's MetaAlmanac.
43
44 let data_folder: PathBuf = [env!("CARGO_MANIFEST_DIR"), "examples", "04_lro_od"]
45 .iter()
46 .collect();
47
48 let meta = data_folder.join("lro-dynamics.dhall");
49
50 // Load this ephem in the general Almanac we're using for this analysis.
51 let mut almanac = MetaAlmanac::new(meta.to_string_lossy().to_string())
52 .map_err(Box::new)?
53 .process(true)
54 .map_err(Box::new)?;
55
56 let mut moon_pc = almanac.planetary_data.get_by_id(MOON)?;
57 moon_pc.mu_km3_s2 = 4902.74987;
58 almanac.planetary_data.set_by_id(MOON, moon_pc)?;
59
60 let mut earth_pc = almanac.planetary_data.get_by_id(EARTH)?;
61 earth_pc.mu_km3_s2 = 398600.436;
62 almanac.planetary_data.set_by_id(EARTH, earth_pc)?;
63
64 // Save this new kernel for reuse.
65 // In an operational context, this would be part of the "Lock" process, and should not change throughout the mission.
66 almanac
67 .planetary_data
68 .save_as(&data_folder.join("lro-specific.pca"), true)?;
69
70 // Lock the almanac (an Arc is a read only structure).
71 let almanac = Arc::new(almanac);
72
73 // Orbit determination requires a Trajectory structure, which can be saved as parquet file.
74 // In our case, the trajectory comes from the BSP file, so we need to build a Trajectory from the almanac directly.
75 // To query the Almanac, we need to build the LRO frame in the J2000 orientation in our case.
76 // Inspecting the LRO BSP in the ANISE GUI shows us that NASA has assigned ID -85 to LRO.
77 let lro_frame = Frame::from_ephem_j2000(-85);
78
79 // To build the trajectory we need to provide a spacecraft template.
80 let sc_template = Spacecraft::builder()
81 .mass(Mass::from_dry_and_prop_masses(1018.0, 900.0)) // Launch masses
82 .srp(SRPData {
83 // SRP configuration is arbitrary, but we will be estimating it anyway.
84 area_m2: 3.9 * 2.7,
85 coeff_reflectivity: 0.96,
86 })
87 .orbit(Orbit::zero(MOON_J2000)) // Setting a zero orbit here because it's just a template
88 .build();
89 // Now we can build the trajectory from the BSP file.
90 // We'll arbitrarily set the tracking arc to 24 hours with a five second time step.
91 let traj_as_flown = Traj::from_bsp(
92 lro_frame,
93 MOON_J2000,
94 almanac.clone(),
95 sc_template,
96 5.seconds(),
97 Some(Epoch::from_str("2024-01-01 00:00:00 UTC")?),
98 Some(Epoch::from_str("2024-01-02 00:00:00 UTC")?),
99 Aberration::LT,
100 Some("LRO".to_string()),
101 )?;
102
103 println!("{traj_as_flown}");
104
105 // ====================== //
106 // === MODEL MATCHING === //
107 // ====================== //
108
109 // Set up the spacecraft dynamics.
110
111 // Specify that the orbital dynamics must account for the graviational pull of the Earth and the Sun.
112 // The gravity of the Moon will also be accounted for since the spaceraft in a lunar orbit.
113 let mut orbital_dyn = OrbitalDynamics::point_masses(vec![EARTH, SUN, JUPITER_BARYCENTER]);
114
115 // We want to include the spherical harmonics, so let's download the gravitational data from the Nyx Cloud.
116 // We're using the GRAIL JGGRX model.
117 let mut jggrx_meta = MetaFile {
118 uri: "http://public-data.nyxspace.com/nyx/models/Luna_jggrx_1500e_sha.tab.gz".to_string(),
119 crc32: Some(0x6bcacda8), // Specifying the CRC32 avoids redownloading it if it's cached.
120 };
121 // And let's download it if we don't have it yet.
122 jggrx_meta.process(true)?;
123
124 // Build the spherical harmonics.
125 // The harmonics must be computed in the body fixed frame.
126 // We're using the long term prediction of the Moon principal axes frame.
127 let moon_pa_frame = MOON_PA_FRAME.with_orient(31008);
128 let sph_harmonics = Harmonics::from_stor(
129 almanac.frame_from_uid(moon_pa_frame)?,
130 HarmonicsMem::from_shadr(&jggrx_meta.uri, 80, 80, true)?,
131 );
132
133 // Include the spherical harmonics into the orbital dynamics.
134 orbital_dyn.accel_models.push(sph_harmonics);
135
136 // We define the solar radiation pressure, using the default solar flux and accounting only
137 // for the eclipsing caused by the Earth and Moon.
138 // Note that by default, enabling the SolarPressure model will also enable the estimation of the coefficient of reflectivity.
139 let srp_dyn = SolarPressure::new(vec![EARTH_J2000, MOON_J2000], almanac.clone())?;
140
141 // Finalize setting up the dynamics, specifying the force models (orbital_dyn) separately from the
142 // acceleration models (SRP in this case). Use `from_models` to specify multiple accel models.
143 let dynamics = SpacecraftDynamics::from_model(orbital_dyn, srp_dyn);
144
145 println!("{dynamics}");
146
147 // Now we can build the propagator.
148 let setup = Propagator::default_dp78(dynamics.clone());
149
150 // For reference, let's build the trajectory with Nyx's models from that LRO state.
151 let (sim_final, traj_as_sim) = setup
152 .with(*traj_as_flown.first(), almanac.clone())
153 .until_epoch_with_traj(traj_as_flown.last().epoch())?;
154
155 println!("SIM INIT: {:x}", traj_as_flown.first());
156 println!("SIM FINAL: {sim_final:x}");
157 // Compute RIC difference between SIM and LRO ephem
158 let sim_lro_delta = sim_final
159 .orbit
160 .ric_difference(&traj_as_flown.last().orbit)?;
161 println!("{traj_as_sim}");
162 println!(
163 "SIM v LRO - RIC Position (m): {:.3}",
164 sim_lro_delta.radius_km * 1e3
165 );
166 println!(
167 "SIM v LRO - RIC Velocity (m/s): {:.3}",
168 sim_lro_delta.velocity_km_s * 1e3
169 );
170
171 traj_as_sim.ric_diff_to_parquet(
172 &traj_as_flown,
173 "./04_lro_sim_truth_error.parquet",
174 ExportCfg::default(),
175 )?;
176
177 // ==================== //
178 // === OD SIMULATOR === //
179 // ==================== //
180
181 // 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
182 // and the truth LRO state.
183
184 // Therefore, we will actually run an estimation from a dispersed LRO state.
185 // The sc_seed is the true LRO state from the BSP.
186 let sc_seed = *traj_as_flown.first();
187
188 // Load the Deep Space Network ground stations.
189 // Nyx allows you to build these at runtime but it's pretty static so we can just load them from YAML.
190 let ground_station_file: PathBuf = [
191 env!("CARGO_MANIFEST_DIR"),
192 "examples",
193 "04_lro_od",
194 "dsn-network.yaml",
195 ]
196 .iter()
197 .collect();
198
199 let devices = GroundStation::load_named(ground_station_file)?;
200
201 let mut proc_devices = devices.clone();
202
203 // Increase the noise in the devices to accept more measurements.
204 for gs in proc_devices.values_mut() {
205 if let Some(noise) = &mut gs
206 .stochastic_noises
207 .as_mut()
208 .unwrap()
209 .get_mut(&MeasurementType::Range)
210 {
211 *noise.white_noise.as_mut().unwrap() *= 3.0;
212 }
213 }
214
215 // Typical OD software requires that you specify your own tracking schedule or you'll have overlapping measurements.
216 // Nyx can build a tracking schedule for you based on the first station with access.
217 let trkconfg_yaml: PathBuf = [
218 env!("CARGO_MANIFEST_DIR"),
219 "examples",
220 "04_lro_od",
221 "tracking-cfg.yaml",
222 ]
223 .iter()
224 .collect();
225
226 let configs: BTreeMap<String, TrkConfig> = TrkConfig::load_named(trkconfg_yaml)?;
227
228 // Build the tracking arc simulation to generate a "standard measurement".
229 let mut trk = TrackingArcSim::<Spacecraft, GroundStation>::with_seed(
230 devices.clone(),
231 traj_as_flown.clone(),
232 configs,
233 123, // Set a seed for reproducibility
234 )?;
235
236 trk.build_schedule(almanac.clone())?;
237 let arc = trk.generate_measurements(almanac.clone())?;
238 // Save the simulated tracking data
239 arc.to_parquet_simple("./04_lro_simulated_tracking.parquet")?;
240
241 // We'll note that in our case, we have continuous coverage of LRO when the vehicle is not behind the Moon.
242 println!("{arc}");
243
244 // Now that we have simulated measurements, we'll run the orbit determination.
245
246 // ===================== //
247 // === OD ESTIMATION === //
248 // ===================== //
249
250 let sc = SpacecraftUncertainty::builder()
251 .nominal(sc_seed)
252 .frame(LocalFrame::RIC)
253 .x_km(0.5)
254 .y_km(0.5)
255 .z_km(0.5)
256 .vx_km_s(5e-3)
257 .vy_km_s(5e-3)
258 .vz_km_s(5e-3)
259 .build();
260
261 // Build the filter initial estimate, which we will reuse in the filter.
262 let mut initial_estimate = sc.to_estimate()?;
263 initial_estimate.covar *= 3.0;
264
265 println!("== FILTER STATE ==\n{sc_seed:x}\n{initial_estimate}");
266
267 // Build the SNC in the Moon J2000 frame, specified as a velocity noise over time.
268 let process_noise = ProcessNoise3D::from_velocity_km_s(
269 &[1e-10, 1e-10, 1e-10],
270 1 * Unit::Hour,
271 10 * Unit::Minute,
272 None,
273 );
274
275 println!("{process_noise}");
276
277 // We'll set up the OD process to reject measurements whose residuals are move than 3 sigmas away from what we expect.
278 let odp = SpacecraftKalmanOD::new(
279 setup,
280 KalmanVariant::ReferenceUpdate,
281 Some(ResidRejectCrit::default()),
282 proc_devices,
283 almanac.clone(),
284 )
285 .with_process_noise(process_noise);
286
287 let od_sol = odp.process_arc(initial_estimate, &arc)?;
288
289 let final_est = od_sol.estimates.last().unwrap();
290
291 println!("{final_est}");
292
293 let ric_err = traj_as_flown
294 .at(final_est.epoch())?
295 .orbit
296 .ric_difference(&final_est.orbital_state())?;
297 println!("== RIC at end ==");
298 println!("RIC Position (m): {:.3}", ric_err.radius_km * 1e3);
299 println!("RIC Velocity (m/s): {:.3}", ric_err.velocity_km_s * 1e3);
300
301 println!(
302 "Num residuals rejected: #{}",
303 od_sol.rejected_residuals().len()
304 );
305 println!(
306 "Percentage within +/-3: {}",
307 od_sol.residual_ratio_within_threshold(3.0).unwrap()
308 );
309 println!("Ratios normal? {}", od_sol.is_normal(None).unwrap());
310
311 od_sol.to_parquet("./04_lro_od_results.parquet", ExportCfg::default())?;
312
313 // In our case, we have the truth trajectory from NASA.
314 // So we can compute the RIC state difference between the real LRO ephem and what we've just estimated.
315 // Export the OD trajectory first.
316 let od_trajectory = od_sol.to_traj()?;
317 // Build the RIC difference.
318 od_trajectory.ric_diff_to_parquet(
319 &traj_as_flown,
320 "./04_lro_od_truth_error.parquet",
321 ExportCfg::default(),
322 )?;
323
324 Ok(())
325}
Source§impl TrackingDataArc
impl TrackingDataArc
Sourcepub fn set_moduli(&mut self, msr_type: MeasurementType, modulus: f64)
pub fn set_moduli(&mut self, msr_type: MeasurementType, modulus: f64)
Set (or overwrites) the modulus of the provided measurement type.
Sourcepub fn apply_moduli(&mut self)
pub fn apply_moduli(&mut self)
Applies the moduli to each measurement, if defined.
Sourcepub fn unique_aliases(&self) -> IndexSet<String>
pub fn unique_aliases(&self) -> IndexSet<String>
Returns the unique list of aliases in this tracking data arc
Sourcepub fn unique_types(&self) -> IndexSet<MeasurementType>
pub fn unique_types(&self) -> IndexSet<MeasurementType>
Returns the unique measurement types in this tracking data arc
Sourcepub fn unique(&self) -> (IndexSet<String>, IndexSet<MeasurementType>)
pub fn unique(&self) -> (IndexSet<String>, IndexSet<MeasurementType>)
Returns the unique trackers and unique measurement types in this data arc
Sourcepub fn start_epoch(&self) -> Option<Epoch>
pub fn start_epoch(&self) -> Option<Epoch>
Returns the start epoch of this tracking arc
Sourcepub fn min_duration_sep(&self) -> Option<Duration>
pub fn min_duration_sep(&self) -> Option<Duration>
Returns the minimum duration between two subsequent measurements.
Sourcepub fn filter_by_epoch<R: RangeBounds<Epoch>>(self, bound: R) -> Self
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.
Sourcepub fn filter_by_offset<R: RangeBounds<Duration>>(self, bound: R) -> Self
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. For example, a bound of 30.minutes()..90.minutes() will only read measurements from the start of the arc + 30 minutes until start + 90 minutes.
Examples found in repository?
34fn main() -> Result<(), Box<dyn Error>> {
35 pel::init();
36
37 // ====================== //
38 // === ALMANAC SET UP === //
39 // ====================== //
40
41 let manifest_dir =
42 PathBuf::from(std::env::var("CARGO_MANIFEST_DIR").unwrap_or(".".to_string()));
43
44 let out = manifest_dir.join("data/04_output/");
45
46 let almanac = Arc::new(
47 Almanac::new(
48 &manifest_dir
49 .join("data/01_planetary/pck08.pca")
50 .to_string_lossy(),
51 )
52 .unwrap()
53 .load(
54 &manifest_dir
55 .join("data/01_planetary/de440s.bsp")
56 .to_string_lossy(),
57 )
58 .unwrap(),
59 );
60
61 let eme2k = almanac.frame_from_uid(EARTH_J2000).unwrap();
62 let moon_iau = almanac.frame_from_uid(IAU_MOON_FRAME).unwrap();
63
64 let epoch = Epoch::from_gregorian_tai(2021, 5, 29, 19, 51, 16, 852_000);
65 let nrho = Orbit::cartesian(
66 166_473.631_302_239_7,
67 -274_715.487_253_382_7,
68 -211_233.210_176_686_7,
69 0.933_451_604_520_018_4,
70 0.436_775_046_841_900_9,
71 -0.082_211_021_250_348_95,
72 epoch,
73 eme2k,
74 );
75
76 let tx_nrho_sc = Spacecraft::from(nrho);
77
78 let state_luna = almanac.transform_to(nrho, MOON_J2000, None).unwrap();
79 println!("Start state (dynamics: Earth, Moon, Sun gravity):\n{state_luna}");
80
81 let bodies = vec![EARTH, SUN];
82 let dynamics = SpacecraftDynamics::new(OrbitalDynamics::point_masses(bodies));
83
84 let setup = Propagator::rk89(
85 dynamics,
86 IntegratorOptions::builder().max_step(0.5.minutes()).build(),
87 );
88
89 /* == Propagate the NRHO vehicle == */
90 let prop_time = 1.1 * state_luna.period().unwrap();
91
92 let (nrho_final, mut tx_traj) = setup
93 .with(tx_nrho_sc, almanac.clone())
94 .for_duration_with_traj(prop_time)
95 .unwrap();
96
97 tx_traj.name = Some("NRHO Tx SC".to_string());
98
99 println!("{tx_traj}");
100
101 /* == Propagate an LLO vehicle == */
102 let llo_orbit =
103 Orbit::try_keplerian_altitude(110.0, 1e-4, 90.0, 0.0, 0.0, 0.0, epoch, moon_iau).unwrap();
104
105 let llo_sc = Spacecraft::builder().orbit(llo_orbit).build();
106
107 let (_, llo_traj) = setup
108 .with(llo_sc, almanac.clone())
109 .until_epoch_with_traj(nrho_final.epoch())
110 .unwrap();
111
112 // Export the subset of the first two hours.
113 llo_traj
114 .clone()
115 .filter_by_offset(..2.hours())
116 .to_parquet_simple(out.join("05_caps_llo_truth.pq"), almanac.clone())?;
117
118 /* == Setup the interlink == */
119
120 let mut measurement_types = IndexSet::new();
121 measurement_types.insert(MeasurementType::Range);
122 measurement_types.insert(MeasurementType::Doppler);
123
124 let mut stochastics = IndexMap::new();
125
126 let sa45_csac_allan_dev = 1e-11;
127
128 stochastics.insert(
129 MeasurementType::Range,
130 StochasticNoise::from_hardware_range_km(
131 sa45_csac_allan_dev,
132 10.0.seconds(),
133 link_specific::ChipRate::StandardT4B,
134 link_specific::SN0::Average,
135 ),
136 );
137
138 stochastics.insert(
139 MeasurementType::Doppler,
140 StochasticNoise::from_hardware_doppler_km_s(
141 sa45_csac_allan_dev,
142 10.0.seconds(),
143 link_specific::CarrierFreq::SBand,
144 link_specific::CN0::Average,
145 ),
146 );
147
148 let interlink = InterlinkTxSpacecraft {
149 traj: tx_traj,
150 measurement_types,
151 integration_time: None,
152 timestamp_noise_s: None,
153 ab_corr: Aberration::LT,
154 stochastic_noises: Some(stochastics),
155 };
156
157 // Devices are the transmitter, which is our NRHO vehicle.
158 let mut devices = BTreeMap::new();
159 devices.insert("NRHO Tx SC".to_string(), interlink);
160
161 let mut configs = BTreeMap::new();
162 configs.insert(
163 "NRHO Tx SC".to_string(),
164 TrkConfig::builder()
165 .strands(vec![Strand {
166 start: epoch,
167 end: nrho_final.epoch(),
168 }])
169 .build(),
170 );
171
172 let mut trk_sim =
173 TrackingArcSim::with_seed(devices.clone(), llo_traj.clone(), configs, 0).unwrap();
174 println!("{trk_sim}");
175
176 let trk_data = trk_sim.generate_measurements(almanac.clone()).unwrap();
177 println!("{trk_data}");
178
179 trk_data
180 .to_parquet_simple(out.clone().join("nrho_interlink_msr.pq"))
181 .unwrap();
182
183 // Run a truth OD where we estimate the LLO position
184 let llo_uncertainty = SpacecraftUncertainty::builder()
185 .nominal(llo_sc)
186 .x_km(1.0)
187 .y_km(1.0)
188 .z_km(1.0)
189 .vx_km_s(1e-3)
190 .vy_km_s(1e-3)
191 .vz_km_s(1e-3)
192 .build();
193
194 let mut proc_devices = devices.clone();
195
196 // Define the initial estimate, randomized, seed for reproducibility
197 let mut initial_estimate = llo_uncertainty.to_estimate_randomized(Some(0)).unwrap();
198 // Inflate the covariance -- https://github.com/nyx-space/nyx/issues/339
199 initial_estimate.covar *= 2.5;
200
201 // Increase the noise in the devices to accept more measurements.
202
203 for link in proc_devices.values_mut() {
204 for noise in &mut link.stochastic_noises.as_mut().unwrap().values_mut() {
205 *noise.white_noise.as_mut().unwrap() *= 3.0;
206 }
207 }
208
209 let init_err = initial_estimate
210 .orbital_state()
211 .ric_difference(&llo_orbit)
212 .unwrap();
213
214 println!("initial estimate:\n{initial_estimate}");
215 println!("RIC errors = {init_err}",);
216
217 let odp = InterlinkKalmanOD::new(
218 setup.clone(),
219 KalmanVariant::ReferenceUpdate,
220 Some(ResidRejectCrit::default()),
221 proc_devices,
222 almanac.clone(),
223 );
224
225 // Shrink the data to process.
226 let arc = trk_data.filter_by_offset(..2.hours());
227
228 let od_sol = odp.process_arc(initial_estimate, &arc).unwrap();
229
230 println!("{od_sol}");
231
232 od_sol
233 .to_parquet(
234 out.join(format!("05_caps_interlink_od_sol.pq")),
235 ExportCfg::default(),
236 )
237 .unwrap();
238
239 let od_traj = od_sol.to_traj().unwrap();
240
241 od_traj
242 .ric_diff_to_parquet(
243 &llo_traj,
244 out.join(format!("05_caps_interlink_llo_est_error.pq")),
245 ExportCfg::default(),
246 )
247 .unwrap();
248
249 let final_est = od_sol.estimates.last().unwrap();
250 assert!(final_est.within_3sigma(), "should be within 3 sigma");
251
252 println!("ESTIMATE\n{final_est:x}\n");
253 let truth = llo_traj.at(final_est.epoch()).unwrap();
254 println!("TRUTH\n{truth:x}");
255
256 let final_err = truth
257 .orbit
258 .ric_difference(&final_est.orbital_state())
259 .unwrap();
260 println!("ERROR {final_err}");
261
262 // Build the residuals versus reference plot.
263 let rvr_sol = odp
264 .process_arc(initial_estimate, &arc.resid_vs_ref_check())
265 .unwrap();
266
267 rvr_sol
268 .to_parquet(
269 out.join(format!("05_caps_interlink_resid_v_ref.pq")),
270 ExportCfg::default(),
271 )
272 .unwrap();
273
274 let final_rvr = rvr_sol.estimates.last().unwrap();
275
276 println!("RMAG error {:.3} m", final_err.rmag_km() * 1e3);
277 println!(
278 "Pure prop error {:.3} m",
279 final_rvr
280 .orbital_state()
281 .ric_difference(&final_est.orbital_state())
282 .unwrap()
283 .rmag_km()
284 * 1e3
285 );
286
287 Ok(())
288}
Sourcepub fn filter_by_tracker(self, tracker: String) -> Self
pub fn filter_by_tracker(self, tracker: String) -> Self
Returns a new tracking arc that only contains measurements from the desired tracker.
Sourcepub fn filter_by_measurement_type(self, included_type: MeasurementType) -> Self
pub fn filter_by_measurement_type(self, included_type: MeasurementType) -> Self
Returns a new tracking arc that only contains measurements of the provided type.
Sourcepub fn exclude_tracker(self, excluded_tracker: String) -> Self
pub fn exclude_tracker(self, excluded_tracker: String) -> Self
Returns a new tracking arc that contains measurements from all trackers except the one provided
Sourcepub fn exclude_by_epoch<R: RangeBounds<Epoch>>(self, bound: R) -> Self
pub fn exclude_by_epoch<R: RangeBounds<Epoch>>(self, bound: R) -> Self
Returns a new tracking arc that excludes measurements within the given epoch range.
Sourcepub fn exclude_measurement_type(self, excluded_type: MeasurementType) -> Self
pub fn exclude_measurement_type(self, excluded_type: MeasurementType) -> Self
Returns a new tracking arc that contains measurements from all trackers except the one provided
Sourcepub fn downsample(self, target_step: Duration) -> Self
pub fn downsample(self, target_step: Duration) -> Self
Downsamples the tracking data to a lower frequency using a simple moving average low-pass filter followed by decimation,
returning new TrackingDataArc
with downsampled measurements.
It provides a computationally efficient approach to reduce the sampling rate while mitigating aliasing effects.
§Algorithm
- A simple moving average filter is applied as a low-pass filter.
- Decimation is performed by selecting every Nth sample after filtering.
§Advantages
- Computationally efficient, suitable for large datasets common in spaceflight applications.
- Provides basic anti-aliasing, crucial for preserving signal integrity in orbit determination and tracking.
- Maintains phase information, important for accurate timing in spacecraft state estimation.
§Limitations
- The frequency response is not as sharp as more sophisticated filters (e.g., FIR, IIR).
- May not provide optimal stopband attenuation for high-precision applications.
§Considerations for Spaceflight Applications
- Suitable for initial data reduction in ground station tracking pipelines.
- Adequate for many orbit determination and tracking tasks where computational speed is prioritized.
- For high-precision applications (e.g., interplanetary navigation), consider using more advanced filtering techniques.
Sourcepub fn resid_vs_ref_check(self) -> Self
pub fn resid_vs_ref_check(self) -> Self
Examples found in repository?
34fn main() -> Result<(), Box<dyn Error>> {
35 pel::init();
36
37 // ====================== //
38 // === ALMANAC SET UP === //
39 // ====================== //
40
41 let manifest_dir =
42 PathBuf::from(std::env::var("CARGO_MANIFEST_DIR").unwrap_or(".".to_string()));
43
44 let out = manifest_dir.join("data/04_output/");
45
46 let almanac = Arc::new(
47 Almanac::new(
48 &manifest_dir
49 .join("data/01_planetary/pck08.pca")
50 .to_string_lossy(),
51 )
52 .unwrap()
53 .load(
54 &manifest_dir
55 .join("data/01_planetary/de440s.bsp")
56 .to_string_lossy(),
57 )
58 .unwrap(),
59 );
60
61 let eme2k = almanac.frame_from_uid(EARTH_J2000).unwrap();
62 let moon_iau = almanac.frame_from_uid(IAU_MOON_FRAME).unwrap();
63
64 let epoch = Epoch::from_gregorian_tai(2021, 5, 29, 19, 51, 16, 852_000);
65 let nrho = Orbit::cartesian(
66 166_473.631_302_239_7,
67 -274_715.487_253_382_7,
68 -211_233.210_176_686_7,
69 0.933_451_604_520_018_4,
70 0.436_775_046_841_900_9,
71 -0.082_211_021_250_348_95,
72 epoch,
73 eme2k,
74 );
75
76 let tx_nrho_sc = Spacecraft::from(nrho);
77
78 let state_luna = almanac.transform_to(nrho, MOON_J2000, None).unwrap();
79 println!("Start state (dynamics: Earth, Moon, Sun gravity):\n{state_luna}");
80
81 let bodies = vec![EARTH, SUN];
82 let dynamics = SpacecraftDynamics::new(OrbitalDynamics::point_masses(bodies));
83
84 let setup = Propagator::rk89(
85 dynamics,
86 IntegratorOptions::builder().max_step(0.5.minutes()).build(),
87 );
88
89 /* == Propagate the NRHO vehicle == */
90 let prop_time = 1.1 * state_luna.period().unwrap();
91
92 let (nrho_final, mut tx_traj) = setup
93 .with(tx_nrho_sc, almanac.clone())
94 .for_duration_with_traj(prop_time)
95 .unwrap();
96
97 tx_traj.name = Some("NRHO Tx SC".to_string());
98
99 println!("{tx_traj}");
100
101 /* == Propagate an LLO vehicle == */
102 let llo_orbit =
103 Orbit::try_keplerian_altitude(110.0, 1e-4, 90.0, 0.0, 0.0, 0.0, epoch, moon_iau).unwrap();
104
105 let llo_sc = Spacecraft::builder().orbit(llo_orbit).build();
106
107 let (_, llo_traj) = setup
108 .with(llo_sc, almanac.clone())
109 .until_epoch_with_traj(nrho_final.epoch())
110 .unwrap();
111
112 // Export the subset of the first two hours.
113 llo_traj
114 .clone()
115 .filter_by_offset(..2.hours())
116 .to_parquet_simple(out.join("05_caps_llo_truth.pq"), almanac.clone())?;
117
118 /* == Setup the interlink == */
119
120 let mut measurement_types = IndexSet::new();
121 measurement_types.insert(MeasurementType::Range);
122 measurement_types.insert(MeasurementType::Doppler);
123
124 let mut stochastics = IndexMap::new();
125
126 let sa45_csac_allan_dev = 1e-11;
127
128 stochastics.insert(
129 MeasurementType::Range,
130 StochasticNoise::from_hardware_range_km(
131 sa45_csac_allan_dev,
132 10.0.seconds(),
133 link_specific::ChipRate::StandardT4B,
134 link_specific::SN0::Average,
135 ),
136 );
137
138 stochastics.insert(
139 MeasurementType::Doppler,
140 StochasticNoise::from_hardware_doppler_km_s(
141 sa45_csac_allan_dev,
142 10.0.seconds(),
143 link_specific::CarrierFreq::SBand,
144 link_specific::CN0::Average,
145 ),
146 );
147
148 let interlink = InterlinkTxSpacecraft {
149 traj: tx_traj,
150 measurement_types,
151 integration_time: None,
152 timestamp_noise_s: None,
153 ab_corr: Aberration::LT,
154 stochastic_noises: Some(stochastics),
155 };
156
157 // Devices are the transmitter, which is our NRHO vehicle.
158 let mut devices = BTreeMap::new();
159 devices.insert("NRHO Tx SC".to_string(), interlink);
160
161 let mut configs = BTreeMap::new();
162 configs.insert(
163 "NRHO Tx SC".to_string(),
164 TrkConfig::builder()
165 .strands(vec![Strand {
166 start: epoch,
167 end: nrho_final.epoch(),
168 }])
169 .build(),
170 );
171
172 let mut trk_sim =
173 TrackingArcSim::with_seed(devices.clone(), llo_traj.clone(), configs, 0).unwrap();
174 println!("{trk_sim}");
175
176 let trk_data = trk_sim.generate_measurements(almanac.clone()).unwrap();
177 println!("{trk_data}");
178
179 trk_data
180 .to_parquet_simple(out.clone().join("nrho_interlink_msr.pq"))
181 .unwrap();
182
183 // Run a truth OD where we estimate the LLO position
184 let llo_uncertainty = SpacecraftUncertainty::builder()
185 .nominal(llo_sc)
186 .x_km(1.0)
187 .y_km(1.0)
188 .z_km(1.0)
189 .vx_km_s(1e-3)
190 .vy_km_s(1e-3)
191 .vz_km_s(1e-3)
192 .build();
193
194 let mut proc_devices = devices.clone();
195
196 // Define the initial estimate, randomized, seed for reproducibility
197 let mut initial_estimate = llo_uncertainty.to_estimate_randomized(Some(0)).unwrap();
198 // Inflate the covariance -- https://github.com/nyx-space/nyx/issues/339
199 initial_estimate.covar *= 2.5;
200
201 // Increase the noise in the devices to accept more measurements.
202
203 for link in proc_devices.values_mut() {
204 for noise in &mut link.stochastic_noises.as_mut().unwrap().values_mut() {
205 *noise.white_noise.as_mut().unwrap() *= 3.0;
206 }
207 }
208
209 let init_err = initial_estimate
210 .orbital_state()
211 .ric_difference(&llo_orbit)
212 .unwrap();
213
214 println!("initial estimate:\n{initial_estimate}");
215 println!("RIC errors = {init_err}",);
216
217 let odp = InterlinkKalmanOD::new(
218 setup.clone(),
219 KalmanVariant::ReferenceUpdate,
220 Some(ResidRejectCrit::default()),
221 proc_devices,
222 almanac.clone(),
223 );
224
225 // Shrink the data to process.
226 let arc = trk_data.filter_by_offset(..2.hours());
227
228 let od_sol = odp.process_arc(initial_estimate, &arc).unwrap();
229
230 println!("{od_sol}");
231
232 od_sol
233 .to_parquet(
234 out.join(format!("05_caps_interlink_od_sol.pq")),
235 ExportCfg::default(),
236 )
237 .unwrap();
238
239 let od_traj = od_sol.to_traj().unwrap();
240
241 od_traj
242 .ric_diff_to_parquet(
243 &llo_traj,
244 out.join(format!("05_caps_interlink_llo_est_error.pq")),
245 ExportCfg::default(),
246 )
247 .unwrap();
248
249 let final_est = od_sol.estimates.last().unwrap();
250 assert!(final_est.within_3sigma(), "should be within 3 sigma");
251
252 println!("ESTIMATE\n{final_est:x}\n");
253 let truth = llo_traj.at(final_est.epoch()).unwrap();
254 println!("TRUTH\n{truth:x}");
255
256 let final_err = truth
257 .orbit
258 .ric_difference(&final_est.orbital_state())
259 .unwrap();
260 println!("ERROR {final_err}");
261
262 // Build the residuals versus reference plot.
263 let rvr_sol = odp
264 .process_arc(initial_estimate, &arc.resid_vs_ref_check())
265 .unwrap();
266
267 rvr_sol
268 .to_parquet(
269 out.join(format!("05_caps_interlink_resid_v_ref.pq")),
270 ExportCfg::default(),
271 )
272 .unwrap();
273
274 let final_rvr = rvr_sol.estimates.last().unwrap();
275
276 println!("RMAG error {:.3} m", final_err.rmag_km() * 1e3);
277 println!(
278 "Pure prop error {:.3} m",
279 final_rvr
280 .orbital_state()
281 .ric_difference(&final_est.orbital_state())
282 .unwrap()
283 .rmag_km()
284 * 1e3
285 );
286
287 Ok(())
288}
Trait Implementations§
Source§impl Add for TrackingDataArc
impl Add for TrackingDataArc
Source§impl AddAssign for TrackingDataArc
impl AddAssign for TrackingDataArc
Source§fn add_assign(&mut self, rhs: Self)
fn add_assign(&mut self, rhs: Self)
+=
operation. Read moreSource§impl Clone for TrackingDataArc
impl Clone for TrackingDataArc
Source§fn clone(&self) -> TrackingDataArc
fn clone(&self) -> TrackingDataArc
1.0.0 · Source§fn clone_from(&mut self, source: &Self)
fn clone_from(&mut self, source: &Self)
source
. Read moreSource§impl Debug for TrackingDataArc
impl Debug for TrackingDataArc
Source§impl Default for TrackingDataArc
impl Default for TrackingDataArc
Source§fn default() -> TrackingDataArc
fn default() -> TrackingDataArc
Source§impl Display for TrackingDataArc
impl Display for TrackingDataArc
Source§impl PartialEq for TrackingDataArc
impl PartialEq for TrackingDataArc
Auto Trait Implementations§
impl Freeze for TrackingDataArc
impl RefUnwindSafe for TrackingDataArc
impl Send for TrackingDataArc
impl Sync for TrackingDataArc
impl Unpin for TrackingDataArc
impl UnwindSafe for TrackingDataArc
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