|
Part I Model-Based Actions: Filtering, Prediction, Smoothing |
|
|
|
1 Kalman Filter, Particle Filter and Other Bayesian Filters |
|
|
3 | (148) |
|
|
3 | (2) |
|
|
5 | (11) |
|
|
7 | (3) |
|
1.2.2 Prediction with the Gauss-Markov Model |
|
|
10 | (2) |
|
1.2.3 Continuation of a Simple Example of Recursive Wiener Filter |
|
|
12 | (4) |
|
|
16 | (16) |
|
|
17 | (4) |
|
1.3.2 Evolution of Filter Variables |
|
|
21 | (6) |
|
1.3.3 Several Perspectives |
|
|
27 | (2) |
|
|
29 | (1) |
|
|
30 | (1) |
|
|
31 | (1) |
|
|
32 | (20) |
|
1.4.1 Propagation and Nonlinearity |
|
|
32 | (7) |
|
1.4.2 Jacobian. Hessian. Change of Coordinates |
|
|
39 | (2) |
|
1.4.3 Local Linearization |
|
|
41 | (2) |
|
1.4.4 Example of a Body Falling Towards Earth |
|
|
43 | (9) |
|
1.5 Extended Kalman Filter (EKF) |
|
|
52 | (10) |
|
|
52 | (6) |
|
1.5.2 Assessment of the Linearized Approximation |
|
|
58 | (4) |
|
1.6 Unscented Kalman Filter (UKF) |
|
|
62 | (14) |
|
1.6.1 The Unscented Transform |
|
|
63 | (7) |
|
1.6.2 The Unscented Kalman Filter (UKF) |
|
|
70 | (6) |
|
|
76 | (24) |
|
1.7.1 An Implementation of the Particle Filter |
|
|
77 | (5) |
|
|
82 | (2) |
|
1.7.3 Multinomial Resampling |
|
|
84 | (2) |
|
1.7.4 Systematic Resampling |
|
|
86 | (1) |
|
1.7.5 Stratified Resampling |
|
|
87 | (1) |
|
1.7.6 Residual Resampling |
|
|
87 | (1) |
|
|
88 | (1) |
|
|
88 | (1) |
|
1.7.9 Basic Theory of the Particle Filter |
|
|
89 | (1) |
|
1.7.10 Sequential Monte Carlo (SMC) |
|
|
90 | (3) |
|
1.7.11 Proposal Importance Functions |
|
|
93 | (4) |
|
1.7.12 Particle Filter Variants |
|
|
97 | (1) |
|
1.7.13 Marginalized Particle Filter (Rao-Blackwellized Particle Filter) |
|
|
98 | (1) |
|
1.7.14 Regularized Particle Filters |
|
|
99 | (1) |
|
1.8 The Perspective of Numerical Integration |
|
|
100 | (13) |
|
|
101 | (1) |
|
|
102 | (3) |
|
1.8.3 Other Approximations |
|
|
105 | (3) |
|
|
108 | (4) |
|
1.8.5 Assumed Density. Expectation Propagation |
|
|
112 | (1) |
|
1.9 Other Bayesian Filters |
|
|
113 | (6) |
|
1.9.1 Ensemble Kalman Filter (EnKF) |
|
|
114 | (1) |
|
1.9.2 Iterative Kalman Filter |
|
|
115 | (1) |
|
1.9.3 Gaussian Particle Filter |
|
|
116 | (1) |
|
|
116 | (2) |
|
|
118 | (1) |
|
1.9.6 Algorithms with Special Characteristics |
|
|
118 | (1) |
|
|
119 | (15) |
|
1.10.1 Optimal Prediction |
|
|
119 | (1) |
|
1.10.2 One-Stage Smoothing |
|
|
120 | (2) |
|
1.10.3 Three Types of Smoothers |
|
|
122 | (10) |
|
1.10.4 Bayesian Smoothing |
|
|
132 | (2) |
|
1.11 Applications of Bayesian Filters |
|
|
134 | (5) |
|
|
134 | (1) |
|
|
134 | (1) |
|
1.11.3 Information Fusion |
|
|
135 | (1) |
|
|
135 | (2) |
|
|
137 | (1) |
|
|
137 | (1) |
|
1.11.7 Earth Monitoring and Forecasting |
|
|
137 | (1) |
|
1.11.8 Energy and Economy |
|
|
138 | (1) |
|
1.11.9 Medical Applications |
|
|
138 | (1) |
|
|
138 | (1) |
|
1.11.11 Other Applications |
|
|
139 | (1) |
|
1.12 Frequently Cited Examples |
|
|
139 | (3) |
|
1.12.1 Bearings-Only Tracking |
|
|
139 | (1) |
|
1.12.2 Other Tracking Cases |
|
|
140 | (1) |
|
1.12.3 Univariate Non-stationary Growth Model |
|
|
140 | (1) |
|
1.12.4 Financial Volatility Model |
|
|
141 | (1) |
|
|
141 | (1) |
|
|
142 | (1) |
|
|
142 | (9) |
|
|
142 | (1) |
|
|
143 | (1) |
|
|
144 | (7) |
|
Part II Sparse Representation. Compressed Sensing |
|
|
|
|
151 | (112) |
|
|
151 | (1) |
|
|
152 | (30) |
|
2.2.1 The Central Problem |
|
|
152 | (3) |
|
|
155 | (1) |
|
2.2.3 Solving Sparsity Optimization Problems |
|
|
156 | (26) |
|
|
182 | (11) |
|
2.3.1 Statement of the Approach |
|
|
182 | (2) |
|
2.3.2 Compression and Recovery. The Matrix A |
|
|
184 | (5) |
|
2.3.3 Incoherence and Sensing |
|
|
189 | (1) |
|
2.3.4 Stable and Robust Recovery |
|
|
190 | (1) |
|
|
191 | (1) |
|
|
192 | (1) |
|
|
193 | (22) |
|
|
193 | (9) |
|
|
202 | (6) |
|
2.4.3 Morphological Components |
|
|
208 | (7) |
|
2.5 An Additional Repertory of Applicable Concepts and Tools |
|
|
215 | (18) |
|
2.5.1 Sparse Representation in MATLAB |
|
|
215 | (4) |
|
|
219 | (9) |
|
2.5.3 Bregman-Related Algorithms |
|
|
228 | (5) |
|
2.6 Matrix Completion and Related Problems |
|
|
233 | (8) |
|
|
234 | (4) |
|
2.6.2 Decomposition of Matrices |
|
|
238 | (3) |
|
|
241 | (9) |
|
2.7.1 Signal Denoising Based on Total Variation (TV) |
|
|
241 | (3) |
|
2.7.2 Picture Reconstruction Based on Matrix Completion |
|
|
244 | (4) |
|
|
248 | (2) |
|
|
250 | (13) |
|
|
250 | (2) |
|
|
252 | (1) |
|
|
253 | (10) |
Appendix A Selected Topics of Mathematical Optimization |
|
263 | (104) |
Appendix B Long Programs |
|
367 | (62) |
Index |
|
429 | |