Kalman filter

Kalman Filter......Page 1
Preface......Page 5
Contents......Page 7
1. Introduction......Page 11
2.1 System–level approaches......Page 13
2.2 Supply parameters......Page 14
3. State–space formulation......Page 15
3.2 Measurement equations......Page 16
3.3 Transition equations......Page 17
3.4 The idea of deviations......Page 18
3.5 The model at a glance......Page 19
3.6 An alternative formulation......Page 20
4.1 Extended Kalman filter......Page 21
4.2 Limiting extended Kalman filter......Page 23
4.3 Unscented Kalman filter......Page 24
5. Application......Page 27
5.1 Methodology......Page 28
5.2 Results......Page 30
7. References......Page 34
1. Introduction......Page 39
2.1 Target tracking system......Page 41
2.2 Kalman controller......Page 42
3.1 Takagi-Sugeno fuzzy models......Page 44
3.2 New fuzzy modeling algorithm......Page 45
3.3 Application......Page 51
5. References......Page 53
2. Training a complex RTRL neural network using EKF......Page 55
3. Recurrent neural networks......Page 56
4. CRTRL learning representation using a state space model......Page 58
5. EKF-CRTRL learning......Page 61
6. Results and conclusion......Page 63
7. References......Page 67
1. Introduction......Page 69
2.1 Inertial motion capture system......Page 70
2.2 UWB location system......Page 71
3.1 Transformation recalculation algorithm......Page 72
3.2 Kalman filter algorithm......Page 74
4.1 Comparison between the fusion algorithms......Page 77
4.2 Application in a human-robot interaction task......Page 79
5. Conclusions......Page 80
7. References......Page 81
1. Introduction......Page 83
2. Problem formulation......Page 85
3. Robust Kalman filter design......Page 86
3. Application to state estimation of a low temperature pressurized water......Page 96
3.1 Dynamical model......Page 97
3.2 Numerical simulation......Page 100
4. Conclusions......Page 103
5.2 Reactor and primary loop......Page 104
5.3 Steam generator......Page 106
6. References......Page 108
1. Introduction......Page 111
2. Active power compensators for improving power quality......Page 112
3. Requirements of the supply voltage for critical loads......Page 114
4.1 Previous works on detection of electrical disturbances......Page 115
4.2 The Kalman filter as voltage estimator and detector of disturbances......Page 117
4.3 The digital recursive RMS calculation method as detector of disturbances......Page 121
4.4 Method for detection of disturbances based on the combination of the KF and......Page 123
4.5 Dependence of the KF-RMS algorithm on the sampling frequency......Page 127
4.6 Experimental tests of the KF-RMS algorithm......Page 128
5.1 Monitoring of the fundamental component in a distorted voltage......Page 130
5.2 Monitoring of the magnitude and phase of the fundamental and harmonic......Page 132
6. Conclusions......Page 134
7. References......Page 135
1. Introduction......Page 137
2. Complex artificial neural network......Page 139
3. Extended complex Kalman filter......Page 140
4. Bad data detection......Page 143
5. Simulation......Page 144
7. References......Page 152
1. Introduction......Page 155
2.2 Vector control scheme of PMSM......Page 156
2.3 Sensorless control......Page 158
3. Modelling of the system......Page 159
2. EKF state estimation......Page 163
3.1 Laboratory setup......Page 165
3.2 Obtained results......Page 166
4. Conclusions......Page 169
5. References......Page 170
1. Introduction......Page 171
2.1 Coordinate systems......Page 174
2.2 Error model for SDINS......Page 175
2.3 Error model of measurements......Page 177
3. Receding horizon Kalman filter for underwater navigation systems......Page 180
4. Simulation......Page 182
7. Appendix......Page 188
8. References......Page 190
1. Introduction......Page 193
2. Coordinate embedding model......Page 194
2.1 Kalman filter equations......Page 196
2.2 Calibration of the Kalman filter......Page 197
3. Validation......Page 201
3.1 Assumption validation......Page 202
3.2 Effective behavior representation......Page 203
3.3 Representativeness of surveyors......Page 204
4. Malicious behavior detection......Page 208
4.1 Anomalous behavior detection method......Page 209
5. Evaluation......Page 210
5.2 Securing Vivaldi......Page 211
5.3 Securing NPS......Page 215
7. References......Page 217
2. Synchronization of chaotic systems......Page 219
3. Chaos synchronization: a stochastic estimation view point......Page 220
3.1 EKF for chaos synchronization......Page 221
3.3 Unscented Kalman filter......Page 223
4. Results and discussion......Page 227
6. References......Page 233
1. Introduction......Page 235
2. Related work......Page 236
3. Overview of our system......Page 237
4.1 Homography......Page 238
4.3 Registration method for a long image sequence......Page 239
5.1 Hierarchical model......Page 240
5.2 Color histogram matching method......Page 242
5.3 The proposed tracking method......Page 243
6.2 Tracking results......Page 247
6.3 Evaluate the homographies......Page 250
9. References......Page 256
1. Introduction......Page 259
2.1 Iterative receivers for channel estimation & data recovery......Page 260
4. System model......Page 261
5.1 Data completely known......Page 262
5.3 Iterative channel estimation......Page 264
6.1 Cyclic FB Kalman......Page 266
7. The FB-Kalman based receivers......Page 267
8.1 A parameter reduction approach......Page 268
8.2 Developing a frequency domain time-variant model......Page 269
8.4 Iterative (data-aided) channel estimation......Page 270
9. Channel estimation in MIMO OFDM systems......Page 271
9.1 MIMO channel model......Page 272
9.2 Input/output equation......Page 273
9.4 Data detection......Page 275
10. Conclusion......Page 277
11. References......Page 278
1. Introduction......Page 281
2. Extended Kalman filter......Page 283
3. Score function selection......Page 284
3.2 p-point M-estimator......Page 285
3.3 Damped Hampel M-estimator......Page 286
4. Re-weighted extended Kalman filter......Page 287
5. Approximate Bayesian extended Kalman filter......Page 290
6. Positioning example......Page 292
7. Simulations and testing......Page 293
7.1 Simulations......Page 294
7.2 Tests using real GPS data......Page 296
8. Conclusions......Page 297
9. References......Page 298
1. Introduction......Page 299
3.1 Window-Matching (WM) methods for motion detection......Page 300
3.2 A WM tracking algorithm based upon similarity distance measurement......Page 301
4. A Kalman filter stage into a WM tracking algorithm......Page 302
5.1 A table tennis sequence1......Page 304
5.2 An urban traffic sequence2......Page 307
5.3 A People Meeting and Walking Sequence3......Page 310
5.4 A Bottle floating on the Sea4......Page 314
6. Conclusions......Page 316
7. References......Page 317
1. Introduction......Page 319
2.1 Range measurement model......Page 321
2.2 Non-line of sight identification......Page 322
3.1 Data smoothing for the NLOS hypothesis testing......Page 324
3.2 Biased Kalman filtering......Page 325
4.1 Problems in NLOS identification using Kalman filter and sliding window......Page 326
4.2 Problem in NLOS mitigation using biased Kalman filter......Page 327
4.3 Functional combination of NLOS identification and mitigation......Page 328
5.1 Extended Kalman filter for TDOA/AOA positioning......Page 330
5.2 Simulations results and discussions......Page 332
7. References......Page 335
1. Introduction......Page 337
2. Problem formulation......Page 338
3. Indoor propagation channel modeling......Page 341
3.2 Interpolation......Page 342
3.3 Adaptive neural fuzzy inference system......Page 344
4. Location estimation method......Page 346
5. Experimental results......Page 348
5.2 Interpolation......Page 349
5.3 Adaptive neural fuzzy inference system......Page 351
5.4 Comparisons......Page 353
8. References......Page 356
1. Introduction......Page 359
2. Engine model......Page 361
3. The estimation of health degradation......Page 362
4.1 Fault detection algorithm for sensor......Page 363
4.2 Fault detection algorithm for actuator......Page 365
5. Simulation results 1......Page 366
5. Simulation results 2......Page 368
7. References......Page 371
1. Introduction......Page 373
2. Strong tracking finite-difference extended Kalman filter......Page 374
2.1 Suboptimal fading extended Kalman filter......Page 375
2.2 Strong tracking finite-difference extended Kalman filter......Page 376
3. STFDEKF based eye tracking and results......Page 378
5. References......Page 380
2.1 Dynamical system......Page 383
2.2 Continuous-time model and discrete-time model......Page 385
3. Unscented Kalman filter......Page 387
3.2 Calculating sigma points......Page 388
3.3 The coordinate transformation problem......Page 390
3.4 Formulation of problem......Page 392
4. Numerical example......Page 394
4.1 Numerical simulation result......Page 395
4.2 Experimental result of the actual system......Page 397
6. References......Page 399