| Line | Branch | Exec | Source |
|---|---|---|---|
| 1 | // This file is part of INSTINCT, the INS Toolkit for Integrated | ||
| 2 | // Navigation Concepts and Training by the Institute of Navigation of | ||
| 3 | // the University of Stuttgart, Germany. | ||
| 4 | // | ||
| 5 | // This Source Code Form is subject to the terms of the Mozilla Public | ||
| 6 | // License, v. 2.0. If a copy of the MPL was not distributed with this | ||
| 7 | // file, You can obtain one at https://mozilla.org/MPL/2.0/. | ||
| 8 | |||
| 9 | /// @file KalmanFilter.cpp | ||
| 10 | /// @brief SPP Kalman Filter | ||
| 11 | /// @author T. Topp (topp@ins.uni-stuttgart.de) | ||
| 12 | /// @date 2023-12-22 | ||
| 13 | |||
| 14 | #include "KalmanFilter.hpp" | ||
| 15 | |||
| 16 | #include <algorithm> | ||
| 17 | #include <functional> | ||
| 18 | #include <variant> | ||
| 19 | |||
| 20 | #include "Navigation/GNSS/Core/SatelliteSystem.hpp" | ||
| 21 | #include "Navigation/GNSS/Positioning/SPP/Keys.hpp" | ||
| 22 | #include "Navigation/GNSS/SystemModel/InterFrequencyBiasModel.hpp" | ||
| 23 | #include "Navigation/GNSS/SystemModel/ReceiverClockModel.hpp" | ||
| 24 | #include "Navigation/GNSS/SystemModel/SystemModel.hpp" | ||
| 25 | #include "internal/gui/widgets/InputWithUnit.hpp" | ||
| 26 | #include "Navigation/Transformations/CoordinateFrames.hpp" | ||
| 27 | #include "util/Logger.hpp" | ||
| 28 | #include <Eigen/src/Core/Matrix.h> | ||
| 29 | #include <fmt/format.h> | ||
| 30 | |||
| 31 | namespace NAV::SPP | ||
| 32 | { | ||
| 33 | 24 | void KalmanFilter::reset(const std::vector<SatelliteSystem>& satelliteSystems) | |
| 34 | { | ||
| 35 | // Covariance of the P matrix initialization velocity uncertainty [m²/s²] | ||
| 36 |
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24 | switch (_gui_initCovarianceVelocityUnit) |
| 37 | { | ||
| 38 | 24 | case InitCovarianceVelocityUnits::m_s: | |
| 39 | 24 | _initCovarianceVelocity = std::pow(_gui_initCovarianceVelocity, 2); | |
| 40 | 24 | break; | |
| 41 | ✗ | case InitCovarianceVelocityUnits::m2_s2: | |
| 42 | ✗ | _initCovarianceVelocity = _gui_initCovarianceVelocity; | |
| 43 | ✗ | break; | |
| 44 | } | ||
| 45 | |||
| 46 | // Covariance of the P matrix initialization clock drift uncertainty [m²/s²] | ||
| 47 |
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24 | switch (_gui_initCovarianceClockDriftUnit) |
| 48 | { | ||
| 49 | ✗ | case InitCovarianceClockDriftUnits::m_s: | |
| 50 | ✗ | _initCovarianceClockDrift = std::pow(_gui_initCovarianceClockDrift, 2); | |
| 51 | ✗ | break; | |
| 52 | 24 | case InitCovarianceClockDriftUnits::s_s: | |
| 53 | 24 | _initCovarianceClockDrift = std::pow(_gui_initCovarianceClockDrift * InsConst::C, 2); | |
| 54 | 24 | break; | |
| 55 | ✗ | case InitCovarianceClockDriftUnits::m2_s2: | |
| 56 | ✗ | _initCovarianceClockDrift = _gui_initCovarianceClockDrift; | |
| 57 | ✗ | break; | |
| 58 | ✗ | case InitCovarianceClockDriftUnits::s2_s2: | |
| 59 | ✗ | _initCovarianceClockDrift = _gui_initCovarianceClockDrift * std::pow(InsConst::C, 2); | |
| 60 | ✗ | break; | |
| 61 | } | ||
| 62 | |||
| 63 | // Covariance of the P matrix initialization inter system clock drift uncertainty [m²/s²] | ||
| 64 |
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24 | switch (_gui_initCovarianceInterSysClockDriftUnit) |
| 65 | { | ||
| 66 | ✗ | case InitCovarianceClockDriftUnits::m_s: | |
| 67 | ✗ | _initCovarianceInterSysClockDrift = std::pow(_gui_initCovarianceInterSysClockDrift, 2); | |
| 68 | ✗ | break; | |
| 69 | 24 | case InitCovarianceClockDriftUnits::s_s: | |
| 70 | 24 | _initCovarianceInterSysClockDrift = std::pow(_gui_initCovarianceInterSysClockDrift * InsConst::C, 2); | |
| 71 | 24 | break; | |
| 72 | ✗ | case InitCovarianceClockDriftUnits::m2_s2: | |
| 73 | ✗ | _initCovarianceInterSysClockDrift = _gui_initCovarianceInterSysClockDrift; | |
| 74 | ✗ | break; | |
| 75 | ✗ | case InitCovarianceClockDriftUnits::s2_s2: | |
| 76 | ✗ | _initCovarianceInterSysClockDrift = _gui_initCovarianceInterSysClockDrift * std::pow(InsConst::C, 2); | |
| 77 | ✗ | break; | |
| 78 | } | ||
| 79 | |||
| 80 | // ########################################################################################################### | ||
| 81 | |||
| 82 | // Reset values to 0 and remove inter system states | ||
| 83 |
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48 | _kalmanFilter = KeyedKalmanFilterD<States::StateKeyType, |
| 84 | 48 | Meas::MeasKeyTypes>{ PosVelKey, {} }; | |
| 85 | |||
| 86 | 24 | _motionModel.initialize(_kalmanFilter.F, _kalmanFilter.W); | |
| 87 | |||
| 88 |
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51 | for (const auto& satSys : satelliteSystems) |
| 89 | { | ||
| 90 |
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27 | _kalmanFilter.addState(Keys::RecvClkBias{ satSys }); |
| 91 |
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27 | _kalmanFilter.addState(Keys::RecvClkDrift{ satSys }); |
| 92 | } | ||
| 93 | 24 | _receiverClockModel.initialize(_kalmanFilter.F, _kalmanFilter.G, _kalmanFilter.W); | |
| 94 | |||
| 95 | LOG_DATA("F = \n{}", _kalmanFilter.F); | ||
| 96 | LOG_DATA("G = \n{}", _kalmanFilter.G); | ||
| 97 | LOG_DATA("W = \n{}", _kalmanFilter.W); | ||
| 98 | 24 | _initialized = false; | |
| 99 | 24 | } | |
| 100 | |||
| 101 | ✗ | void KalmanFilter::initialize(const KeyedVectorXd<States::StateKeyType>& states, const KeyedMatrixXd<States::StateKeyType, States::StateKeyType>& variance) | |
| 102 | { | ||
| 103 | LOG_DATA("x_KF(pre-init) = \n{}", _kalmanFilter.x.transposed()); | ||
| 104 | ✗ | _kalmanFilter.x(states.rowKeys()) = states(all); | |
| 105 | ✗ | _kalmanFilter.P(variance.rowKeys(), variance.colKeys()) = variance(all, all) * 100.0; // LSQ variance is very small. So make bigger | |
| 106 | |||
| 107 | ✗ | if (!states.hasAnyRows(VelKey)) // We always estimate velocity in the KF, but LSQ could not, so set a default value | |
| 108 | { | ||
| 109 | ✗ | _kalmanFilter.P(VelKey, VelKey).diagonal() << Eigen::Vector3d::Ones() * _initCovarianceVelocity; | |
| 110 | } | ||
| 111 | // We always estimate receiver clock drift in the KF, but LSQ could not, so set a default value | ||
| 112 | ✗ | for (const auto& key : _kalmanFilter.P.rowKeys()) | |
| 113 | { | ||
| 114 | ✗ | if (std::holds_alternative<Keys::RecvClkDrift>(key) && !states.hasRow(key)) | |
| 115 | { | ||
| 116 | ✗ | _kalmanFilter.P(key, key) = _initCovarianceClockDrift; | |
| 117 | } | ||
| 118 | } | ||
| 119 | |||
| 120 | ✗ | _initialized = true; | |
| 121 | ✗ | } | |
| 122 | |||
| 123 | ✗ | void KalmanFilter::deinitialize() | |
| 124 | { | ||
| 125 | ✗ | _initialized = false; | |
| 126 | ✗ | } | |
| 127 | |||
| 128 | ✗ | void KalmanFilter::predict(const double& dt, const Eigen::Vector3d& lla_pos, [[maybe_unused]] const std::string& nameId) | |
| 129 | { | ||
| 130 | // Update the State transition matrix (𝚽) and the Process noise covariance matrix (𝐐) | ||
| 131 | |||
| 132 | LOG_DATA("{}: F =\n{}", nameId, _kalmanFilter.F); | ||
| 133 | LOG_DATA("{}: G =\n{}", nameId, _kalmanFilter.G); | ||
| 134 | LOG_DATA("{}: W =\n{}", nameId, _kalmanFilter.W); | ||
| 135 | |||
| 136 | ✗ | _motionModel.updatePhiAndQ(_kalmanFilter.Phi, | |
| 137 | ✗ | _kalmanFilter.Q, | |
| 138 | ✗ | _kalmanFilter.G, | |
| 139 | ✗ | _kalmanFilter.F, | |
| 140 | ✗ | _kalmanFilter.W, | |
| 141 | dt, | ||
| 142 | ✗ | lla_pos(0), | |
| 143 | ✗ | lla_pos(1), | |
| 144 | _systemModelCalcAlgorithm); | ||
| 145 | |||
| 146 | ✗ | _receiverClockModel.updatePhiAndQ(_kalmanFilter.Phi, | |
| 147 | ✗ | _kalmanFilter.Q, | |
| 148 | ✗ | _kalmanFilter.F, | |
| 149 | ✗ | _kalmanFilter.G, | |
| 150 | ✗ | _kalmanFilter.W, | |
| 151 | dt, | ||
| 152 | _systemModelCalcAlgorithm); | ||
| 153 | |||
| 154 | ✗ | _interFrequencyBiasModel.updatePhiAndQ(_kalmanFilter.Phi, | |
| 155 | ✗ | _kalmanFilter.Q, | |
| 156 | dt); | ||
| 157 | |||
| 158 | LOG_DATA("{}: Phi =\n{}", nameId, _kalmanFilter.Phi); | ||
| 159 | LOG_DATA("{}: Q =\n{}", nameId, _kalmanFilter.Q); | ||
| 160 | |||
| 161 | LOG_DATA("{}: P (a posteriori) =\n{}", nameId, _kalmanFilter.P); | ||
| 162 | LOG_DATA("{}: x (a posteriori) =\n{}", nameId, _kalmanFilter.x.transposed()); | ||
| 163 | ✗ | _kalmanFilter.predict(); | |
| 164 | LOG_DATA("{}: x (a priori ) =\n{}", nameId, _kalmanFilter.x.transposed()); | ||
| 165 | LOG_DATA("{}: P (a priori ) =\n{}", nameId, _kalmanFilter.P); | ||
| 166 | ✗ | } | |
| 167 | |||
| 168 | ✗ | void KalmanFilter::update(const std::vector<Meas::MeasKeyTypes>& measKeys, | |
| 169 | const KeyedMatrixXd<Meas::MeasKeyTypes, States::StateKeyType>& H, | ||
| 170 | const KeyedMatrixXd<Meas::MeasKeyTypes, Meas::MeasKeyTypes>& R, | ||
| 171 | const KeyedVectorXd<Meas::MeasKeyTypes>& dz, | ||
| 172 | [[maybe_unused]] const std::string& nameId) | ||
| 173 | { | ||
| 174 | LOG_DATA("{}: called", nameId); | ||
| 175 | |||
| 176 | ✗ | _kalmanFilter.setMeasurements(measKeys); | |
| 177 | |||
| 178 | ✗ | _kalmanFilter.H = H; | |
| 179 | ✗ | _kalmanFilter.R = R; | |
| 180 | ✗ | _kalmanFilter.z = dz; | |
| 181 | |||
| 182 | ✗ | _kalmanFilter.correctWithMeasurementInnovation(); | |
| 183 | ✗ | } | |
| 184 | |||
| 185 | 10 | void KalmanFilter::addInterFrequencyBias(const Frequency& freq) | |
| 186 | { | ||
| 187 | 10 | auto bias = Keys::InterFreqBias{ freq }; | |
| 188 |
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10 | _kalmanFilter.addState(bias); |
| 189 |
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10 | _kalmanFilter.P(bias, bias) = _kalmanFilter.P(Keys::RecvClkBias{ freq.getSatSys() }, |
| 190 |
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10 | Keys::RecvClkBias{ freq.getSatSys() }); |
| 191 | 10 | _interFrequencyBiasModel.initialize(bias, | |
| 192 | 10 | _kalmanFilter.F, | |
| 193 | 10 | _kalmanFilter.G, | |
| 194 |
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10 | _kalmanFilter.W); |
| 195 | 10 | } | |
| 196 | |||
| 197 | ✗ | bool KalmanFilter::ShowGuiWidgets(const char* id, bool useDoppler, bool multiConstellation, bool estimateInterFrequencyBiases, float itemWidth, float unitWidth) | |
| 198 | { | ||
| 199 | ✗ | bool changed = false; | |
| 200 | |||
| 201 | ✗ | itemWidth -= ImGui::GetStyle().IndentSpacing; | |
| 202 | ✗ | float configWidth = itemWidth + unitWidth; | |
| 203 | |||
| 204 | ✗ | ImGui::SetNextItemOpen(true, ImGuiCond_FirstUseEver); | |
| 205 | ✗ | if (ImGui::TreeNode(fmt::format("System/Process noise##{}", id).c_str())) | |
| 206 | { | ||
| 207 | ✗ | changed |= SystemModelGui(_systemModelCalcAlgorithm, itemWidth, id); | |
| 208 | |||
| 209 | ✗ | changed |= _motionModel.ShowGui(configWidth, unitWidth, id); | |
| 210 | ✗ | changed |= _receiverClockModel.ShowGui(configWidth, unitWidth, id); | |
| 211 | ✗ | if (estimateInterFrequencyBiases) | |
| 212 | { | ||
| 213 | ✗ | changed |= _interFrequencyBiasModel.ShowGui(configWidth, unitWidth, id); | |
| 214 | } | ||
| 215 | |||
| 216 | ✗ | ImGui::TreePop(); | |
| 217 | } | ||
| 218 | |||
| 219 | ✗ | if (!useDoppler) | |
| 220 | { | ||
| 221 | ✗ | ImGui::SetNextItemOpen(true, ImGuiCond_FirstUseEver); | |
| 222 | ✗ | if (ImGui::TreeNode(fmt::format("Initial Error Covariance Matrix P##{}", id).c_str())) | |
| 223 | { | ||
| 224 | ✗ | if (gui::widgets::InputDoubleWithUnit(fmt::format("{} of the velocity uncertainty##{}", | |
| 225 | ✗ | _gui_initCovarianceVelocityUnit == InitCovarianceVelocityUnits::m_s ? "Standard deviation" : "Variance", id) | |
| 226 | .c_str(), | ||
| 227 | ✗ | configWidth, unitWidth, &_gui_initCovarianceVelocity, _gui_initCovarianceVelocityUnit, "m/s\0m^2/s^2\0\0", | |
| 228 | 0.0, 0.0, "%.2e", ImGuiInputTextFlags_CharsScientific)) | ||
| 229 | { | ||
| 230 | ✗ | LOG_DEBUG("{}: _gui_initCovarianceVelocity changed to {}", id, _gui_initCovarianceVelocity); | |
| 231 | ✗ | LOG_DEBUG("{}: _gui_initCovarianceVelocityUnit changed to {}", id, fmt::underlying(_gui_initCovarianceVelocityUnit)); | |
| 232 | ✗ | changed = true; | |
| 233 | } | ||
| 234 | ✗ | if (gui::widgets::InputDoubleWithUnit(fmt::format("{} of the clock drift uncertainty##{}", | |
| 235 | ✗ | (_gui_initCovarianceClockDriftUnit == InitCovarianceClockDriftUnits::m_s | |
| 236 | ✗ | || _gui_initCovarianceClockDriftUnit == InitCovarianceClockDriftUnits::s_s) | |
| 237 | ✗ | ? "Standard deviation" | |
| 238 | : "Variance", | ||
| 239 | id) | ||
| 240 | .c_str(), | ||
| 241 | ✗ | configWidth, unitWidth, &_gui_initCovarianceClockDrift, _gui_initCovarianceClockDriftUnit, "m/s\0s/s\0m^2/s^2\0s^2/s^2\0\0", | |
| 242 | 0.0, 0.0, "%.2e", ImGuiInputTextFlags_CharsScientific)) | ||
| 243 | { | ||
| 244 | ✗ | LOG_DEBUG("{}: _gui_initCovarianceClockDrift changed to {}", id, _gui_initCovarianceClockDrift); | |
| 245 | ✗ | LOG_DEBUG("{}: _gui_initCovarianceClockDriftUnit changed to {}", id, fmt::underlying(_gui_initCovarianceClockDriftUnit)); | |
| 246 | ✗ | changed = true; | |
| 247 | } | ||
| 248 | ✗ | if (multiConstellation | |
| 249 | ✗ | && gui::widgets::InputDoubleWithUnit(fmt::format("{} of the inter system clock drift uncertainty##{}", | |
| 250 | ✗ | (_gui_initCovarianceInterSysClockDriftUnit == InitCovarianceClockDriftUnits::m_s | |
| 251 | ✗ | || _gui_initCovarianceInterSysClockDriftUnit == InitCovarianceClockDriftUnits::s_s) | |
| 252 | ✗ | ? "Standard deviation" | |
| 253 | : "Variance", | ||
| 254 | id) | ||
| 255 | .c_str(), | ||
| 256 | ✗ | configWidth, unitWidth, &_gui_initCovarianceInterSysClockDrift, _gui_initCovarianceInterSysClockDriftUnit, "m/s\0s/s\0m^2/s^2\0s^2/s^2\0\0", | |
| 257 | 0.0, 0.0, "%.2e", ImGuiInputTextFlags_CharsScientific)) | ||
| 258 | { | ||
| 259 | ✗ | LOG_DEBUG("{}: _gui_initCovarianceInterSysClockDrift changed to {}", id, _gui_initCovarianceInterSysClockDrift); | |
| 260 | ✗ | LOG_DEBUG("{}: _gui_initCovarianceInterSysClockDriftUnit changed to {}", id, fmt::underlying(_gui_initCovarianceInterSysClockDriftUnit)); | |
| 261 | ✗ | changed = true; | |
| 262 | } | ||
| 263 | |||
| 264 | ✗ | ImGui::TreePop(); | |
| 265 | } | ||
| 266 | } | ||
| 267 | |||
| 268 | ✗ | ImGui::SetNextItemOpen(false, ImGuiCond_FirstUseEver); | |
| 269 | ✗ | if (ImGui::TreeNode(fmt::format("Kalman Filter matrices##{}", id).c_str())) | |
| 270 | { | ||
| 271 | ✗ | _kalmanFilter.showKalmanFilterMatrixViews(id); | |
| 272 | ✗ | ImGui::TreePop(); | |
| 273 | } | ||
| 274 | |||
| 275 | ✗ | return changed; | |
| 276 | } | ||
| 277 | |||
| 278 | ✗ | void KalmanFilter::setClockBiasErrorCovariance(double clkPhaseDrift) | |
| 279 | { | ||
| 280 | ✗ | for (const auto& state : _kalmanFilter.P.rowKeys()) | |
| 281 | { | ||
| 282 | ✗ | if (const auto* bias = std::get_if<Keys::RecvClkBias>(&state)) | |
| 283 | { | ||
| 284 | ✗ | _kalmanFilter.P(*bias, *bias) = clkPhaseDrift; | |
| 285 | } | ||
| 286 | } | ||
| 287 | ✗ | } | |
| 288 | |||
| 289 | ✗ | const std::vector<SPP::States::StateKeyType>& KalmanFilter::getStateKeys() const | |
| 290 | { | ||
| 291 | ✗ | return _kalmanFilter.x.rowKeys(); | |
| 292 | } | ||
| 293 | |||
| 294 | ✗ | const KeyedVectorX<double, States::StateKeyType>& KalmanFilter::getState() const | |
| 295 | { | ||
| 296 | ✗ | return _kalmanFilter.x; | |
| 297 | } | ||
| 298 | |||
| 299 | ✗ | const KeyedMatrixXd<States::StateKeyType, States::StateKeyType>& KalmanFilter::getErrorCovarianceMatrix() const | |
| 300 | { | ||
| 301 | ✗ | return _kalmanFilter.P; | |
| 302 | } | ||
| 303 | |||
| 304 | ✗ | void to_json(json& j, const KalmanFilter& data) | |
| 305 | { | ||
| 306 | j = { | ||
| 307 | ✗ | { "systemModelCalcAlgorithm", data._systemModelCalcAlgorithm }, | |
| 308 | ✗ | { "motionModel", data._motionModel }, | |
| 309 | ✗ | { "receiverClockModel", data._receiverClockModel }, | |
| 310 | ✗ | { "interFrequencyBiasModel", data._interFrequencyBiasModel }, | |
| 311 | ✗ | }; | |
| 312 | ✗ | } // namespace NAV::SPP | |
| 313 | |||
| 314 | 8 | void from_json(const json& j, KalmanFilter& data) | |
| 315 | { | ||
| 316 |
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8 | if (j.contains("systemModelCalcAlgorithm")) { j.at("systemModelCalcAlgorithm").get_to(data._systemModelCalcAlgorithm); } |
| 317 |
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8 | if (j.contains("motionModel")) { j.at("motionModel").get_to(data._motionModel); } |
| 318 |
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8 | if (j.contains("receiverClockModel")) { j.at("receiverClockModel").get_to(data._receiverClockModel); } |
| 319 |
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8 | if (j.contains("interFrequencyBiasModel")) { j.at("interFrequencyBiasModel").get_to(data._interFrequencyBiasModel); } |
| 320 | 8 | } | |
| 321 | |||
| 322 | } // namespace NAV::SPP | ||
| 323 |