Optimal and Robust State Estimation: Finite Impulse Response (FIR) and Kalman Approaches
Finite Impulse Response (FIR) and Kalman Approaches
1st Edition
Published on 25. September 2022
Book
Hardback
480 pages
978-1-119-86307-6 (ISBN)
Description
A unified and systematic theoretical framework for solving problems related to finite impulse response (FIR) estimate
Optimal and Robust State Estimation: Finite Impulse Response (FIR) and Kalman Approaches is a comprehensive investigation into batch state estimators and recursive forms. The work begins by introducing the reader to the state estimation approach and provides a brief historical overview. Next, the work discusses the specific properties of finite impulse response (FIR) state estimators. Further chapters give the basics of probability and stochastic processes, discuss the available linear and nonlinear state estimators, deal with optimal FIR filtering, and consider a limited memory batch and recursive algorithms.
Other topics covered include solving the q-lag FIR smoothing problem, introducing the receding horizon (RH) FIR state estimation approach, and developing the theory of FIR state estimation under disturbances. The book closes by discussing the theory of FIR state estimation for uncertain systems and providing several applications where the FIR state estimators are used effectively. Key concepts covered in the work include:
A holistic overview of the state estimation approach, which arose from the need to know the internal state of a real system, given that the input and output are both known
Optimal, optimal unbiased, maximum likelihood, and unbiased and robust finite impulse response (FIR) structures
FIR state estimation approach along with the infinite impulse response (IIR) and Kalman approaches
Cost functions and the most critical properties of FIR and IIR state estimates
Optimal and Robust State Estimation: Finite Impulse Response (FIR) and Kalman Approaches was written for professionals in the fields of microwave engineering, system engineering, and robotics who wish to move towards solving finite impulse response (FIR) estimate issues in both theoretical and practical applications. Graduate and senior undergraduate students with coursework dealing with state estimation will also be able to use the book to gain a valuable foundation of knowledge and become more adept in their chosen fields of study.
Optimal and Robust State Estimation: Finite Impulse Response (FIR) and Kalman Approaches is a comprehensive investigation into batch state estimators and recursive forms. The work begins by introducing the reader to the state estimation approach and provides a brief historical overview. Next, the work discusses the specific properties of finite impulse response (FIR) state estimators. Further chapters give the basics of probability and stochastic processes, discuss the available linear and nonlinear state estimators, deal with optimal FIR filtering, and consider a limited memory batch and recursive algorithms.
Other topics covered include solving the q-lag FIR smoothing problem, introducing the receding horizon (RH) FIR state estimation approach, and developing the theory of FIR state estimation under disturbances. The book closes by discussing the theory of FIR state estimation for uncertain systems and providing several applications where the FIR state estimators are used effectively. Key concepts covered in the work include:
A holistic overview of the state estimation approach, which arose from the need to know the internal state of a real system, given that the input and output are both known
Optimal, optimal unbiased, maximum likelihood, and unbiased and robust finite impulse response (FIR) structures
FIR state estimation approach along with the infinite impulse response (IIR) and Kalman approaches
Cost functions and the most critical properties of FIR and IIR state estimates
Optimal and Robust State Estimation: Finite Impulse Response (FIR) and Kalman Approaches was written for professionals in the fields of microwave engineering, system engineering, and robotics who wish to move towards solving finite impulse response (FIR) estimate issues in both theoretical and practical applications. Graduate and senior undergraduate students with coursework dealing with state estimation will also be able to use the book to gain a valuable foundation of knowledge and become more adept in their chosen fields of study.
More details
Language
English
Place of publication
Hoboken
United States
Publishing group
John Wiley and Sons Ltd
Target group
Professional and scholarly
Dimensions
Height: 257 mm
Width: 185 mm
Thickness: 31 mm
Weight
1026 gr
ISBN-13
978-1-119-86307-6 (9781119863076)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
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Book
11/2023
Wiley-Blackwell
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Additional editions
Yuriy S. Shmaliy | Shunyi Zhao
Optimal and Robust State Estimation
Finite Impulse Response (FIR) and Kalman Approaches
E-Book
07/2022
1st Edition
Wiley
€118.99
Available for download
Yuriy S. Shmaliy | Shunyi Zhao
Optimal and Robust State Estimation
Finite Impulse Response (FIR) and Kalman Approaches
E-Book
07/2022
1st Edition
Wiley
€118.99
Available for download
Person
YURIY S. SHMALIY, PhD, is a Professor with the Universidad de Guanajuato, Mexico. He serves as an Editorial Board Member in various scientific journals and is an IEEE Fellow. He also developed the theory of FIR state estimation, gave many keynote and plenary lectures, and his discrete orthogonal polynomials are called discrete Shmaliy moments.
SHUNYI ZHAO, PhD, is a Professor with the Jiangnan University, China. His current research interests include statistical signal processing, Bayesian estimation theory, and fault detection and diagnosis.
SHUNYI ZHAO, PhD, is a Professor with the Jiangnan University, China. His current research interests include statistical signal processing, Bayesian estimation theory, and fault detection and diagnosis.
Content
1 Introduction 1
1.1 What is System State? 2
1.1.1 Why and How do We Estimate State? 2
1.1.2 What Model to Estimate State? 3
1.1.3 What are Basic State Estimates in Discrete Time? 5
1.2 Properties of State Estimators 6
1.2.1 Structures and Types 6
1.2.2 Optimality 10
1.2.3 Unbiased Optimality (Maximum Likelihood) 11
1.2.4 Suboptimality 14
1.2.5 Unbiasedness 17
1.2.6 Deadbeat 17
1.2.7 Denoising (Noise Power Gain) 17
1.2.8 Stability 18
1.2.9 Robustness 18
1.2.10 Computational Complexity 19
1.2.11 Memory Use 20
1.3 More About FIR State Estimators 20
1.4 Historical Overview and Most Noticeable Works 21
1.5 Summary 26
1.6 Problems 27
2 Probability and Stochastic Processes 31
2.1 Random Variables 31
2.1.1 Moments and Cumulants 33
2.1.2 Product Moments 39
2.1.3 Vector Random Variables 41
2.1.4 Conditional Probability. Bayes' Rule 42
2.1.5 Transformation of Random Variables 45
2.2 Stochastic Processes 47
2.2.1 Correlation Function 48
2.2.2 Power Spectral Density 51
2.2.3 Gaussian Processes 53
2.2.4 White Gaussian Noise 55
2.2.5 Markov Processes 57
2.3 Stochastic Differential Equation 60
2.3.1 Standard Stochastic Differential Equation 61
2.3.2 Ito and Stratonovich Stochastic Calculus 61
2.3.3 Diffusion Process Interpretation 62
2.3.4 Fokker-Planck-Kolmogorov Equation 63
2.3.5 Langevin Equation 64
2.4 Summary 65
2.5 Problems 66
3 State Estimation 71
3.1 Lineal Stochastic Process in State Space 71
3.1.1 Continuous-Time Model 73
3.1.2 Discrete-Time Model 77
3.2 Methods of Linear State Estimation 81
3.2.1 Bayesian Estimator 82
3.2.2 Maximum Likelihood Estimator 85
3.2.3 Least Squares Estimator 86
3.2.4 Unbiased Estimator 87
3.2.5 Kalman Filtering Algorithm 88
3.2.6 Backward Kalman Filter 94
3.2.7 Alternative Forms of Kalman Filter 96
3.2.8 General Kalman Filter 98
3.2.9 Kalman-Bucy Filter 110
3.3 Linear Recursive Smoothing 113
3.3.1 Rauch-Tung-Striebel Algorithm 113
3.3.2 Bryson-Frazier Algorithm 114
3.3.3 Two-Filter (Forward-Backward) Smoothing 115
3.4 Nonlinear Models and Estimators 116
3.4.1 Extended Kalman Filter 117
3.4.2 Unscented Kalman Filter 119
3.4.3 Particle Filtering 122
3.5 Robust State Estimation 126
3.5.1 Robustified Kalman Filter 127
3.5.2 Robust Kalman Filter 128
3.5.3 H8 Filtering 131
3.5.4 Game Theory H8 Filter 132
3.6 Summary 133
3.7 Problems 134
4 Optimal FIR and Limited Memory Filtering 139
4.1 Extended State-Space Model 140
4.2 The a posteriori Optimal FIR Filter 142
4.2.1 Batch Estimate and Error Covariance 143
4.2.2 Recursive Forms 145
4.2.3 System Identification 149
4.3 The a posteriori Optimal Unbiased FIR Filter 149
4.3.1 Batch OUFIR-I Estimate and Error Covariance 150
4.3.2 Recursive Forms for OUFIR-I Filter 151
4.3.3 Batch OUFIR-II Estimate and Error Covariance 153
4.3.4 Recursion Forms for OUFIR-II Filter 154
4.4 Maximum Likelihood FIR Estimator 158
4.4.1 ML-I FIR Filtering Estimate 158
4.4.2 Equivalence of ML-I FIR and OUFIR Filters 159
4.4.3 ML-II FIR Filtering Estimate 162
4.4.4 Properties of ML FIR State Estimators 163
4.5 The a priori FIR Filters 164
4.5.1 The a priori Optimal FIR Filter 164
4.5.2 The a priori Optimal Unbiased FIR Filter 165
4.6 Limited Memory Filtering 165
4.6.1 Batch Limited Memory Filter 166
4.6.2 Iterative LMF Algorithm using Recursions 168
4.7 Continuous-Time Optimal FIR Filter 169
4.7.1 Optimal Impulse Response 169
4.7.2 Differential Equation Form 171
4.8 Extended a posteriori OFIR Filtering 172
4.9 Properties of FIR State Estimators 174
4.10 Summary 179
4.11 Problems 182
5 Optimal FIR Smoothing 187
5.1 Introduction 187
5.2 Smoothing Problem 188
5.3 Forward Filter/Forward Model q-lag OFIR Smoothing 189
5.3.1 Batch Smoothing Estimate 190
5.3.2 Error Covariance 193
5.4 Backward OFIR Filtering 195
5.4.1 Backward State-Space Model 195
5.4.2 Batch Estimate 196
5.4.3 Recursive Estimate and Error Covariance 198
5.5 Backward Filter/Backward Model g-lag OFIR Smoother 202
5.5.1 Batch Smoothing Estimate 203
5.5.2 Error Covariance 204
5.6 Forward Filter/Backward Model q-Lag OFIR Smoother 205
5.6.1 Batch Smoothing Estimate 205
5.6.2 Error Covariance 208
5.7 Backward Filter/Forward Model q-Lag OFIR Smoother 208
5.7.1 Batch Smoothing Estimate 208
5.7.2 Error Covariance 211
5.8 Two-Filter q-lag OFIR Smoother 213
5.9 q-Lag ML FIR Smoothing 214
5.9.1 Batch q-lag ML FIR Estimate 215
5.9.2 Error Covariance 216
5.10 Summary 216
5.11 Problems 217
6 Unbiased FIR State Estimation 221
6.1 Introduction 221
6.2 The a posteriori UFIR Filter 222
6.2.1 Batch Form 222
6.2.2 Iterative Algorithm Using Recursions 224
6.2.3 Recursive Error Covariance 226
6.2.4 Optimal Averaging Horizon 228
6.3 Backward a posteriori UFIR Filter 234
6.3.1 Batch Form 235
6.3.2 Recursions and Iterative Algorithm 236
6.3.3 Recursive Error Covariance 239
6.4 The q-lag UFIR Smoother 240
6.4.1 Batch and Recursive Forms 240
6.4.2 Error Covariance 242
6.4.3 Equivalence of UFIR Smoothers 244
6.5 State Estimation using Polynomial Models 245
6.5.1 Problems Solved with UFIR Structures 246
6.5.2 The p-shift UFIR Filter 247
6.5.3 Filtering of Polynomial Models 250
6.5.4 Discrete Shmaliy Moments 252
6.5.5 Smoothing Filtering and Smoothing 252
6.5.6 Generalized Savitzky-Golay Filter 254
6.5.7 Predictive Filtering and Prediction 255
6.6 UFIR State Estimation under Colored Noise 256
6.6.1 Colored Measurement Noise 256
6.6.2 Colored Process Noise 259
6.7 Extended UFIR Filtering 262
6.7.1 First-Order Extended UFIR Filter 263
6.7.2 Second-Order Extended UFIR Filter 263
6.8 Robustness of UFIR Filter 266
6.8.1 Errors in Noise Covariances and Weighted Matrices 268
6.8.2 Model Errors 271
6.8.3 Temporary Uncertainties 274
6.9 Implementation of Polynomial UFIR Filters 276
6.9.1 Filter Structures in z-Domain 276
6.9.2 Transfer Function in DFT Domain 282
6.10 Summary 287
6.11 Problems 288
7 FIR Prediction and Receding Horizon Filtering 295
7.1 Introduction 295
7.2 Prediction Strategies 296
7.2.1 Kalman Predictor 296
7.3 Extended Predictive State-Space Model 298
7.4 UFIR Predictor 298
7.4.1 Batch UFIR Predictor 299
7.4.2 Iterative Algorithm using Recursions 299
7.4.3 Recursive Error Covariance 303
7.5 Optimal FIR Predictor 304
7.5.1 Batch Estimate and Error Covariance 305
7.5.2 Recursive Forms and Iterative Algorithm 306
7.6 Receding Horizon FIR Filtering 308
7.6.1 MVF-I Filter for Stationary Processes 309
7.6.2 MVF-II Filter for Nonstationary Processes 311
7.7 Maximum Likelihood FIR Predictor 313
7.7.1 ML-I FIR Predictor 314
7.7.2 ML-II FIR Predictor 315
7.8 Extended OFIR Prediction 315
7.9 Summary 317
7.10 Problems 318
8 Robust FIR State Estimation under Disturbances 323
8.1 Extended Models under Disturbances 324
8.2 The a posteriori H2 FIR Filtering 326
8.2.1 H2-OFIR Filter 328
8.2.2 Optimal Unbiased H2 FIR Filter 330
8.2.3 Suboptimal H2 FIR Filtering Algorithms 336
8.3 H2 FIR Prediction 338
8.3.1 H2-OFIR Predictor 339
8.3.2 Bias-constrained H2-OUFIR Predictor 341
8.3.3 Suboptimal H2 FIR Predictive Algorithms 341
8.3.4 Receding Horizon H2-MVF Filter 343
8.4 H8 FIR State Estimation 344
8.4.1 The a posteriori H8 FIR Filter 346
8.4.2 H8 FIR Predictor 350
8.5 H2{H8 FIR Filter and Predictor 354
8.6 Generalized H2 FIR State Estimation 355
8.6.1 Energy-to-Peak Lemma 355
8.6.2 L2-to-L8 FIR Filter and Predictor 359
8.7 L1 FIR State Estimation 362
8.7.1 Peak-to-Peak Lemma 363
8.7.2 L8-to-L8 FIR Filtering and Prediction 365
8.8 Game Theory FIR State Estimation 367
8.8.1 The a posteriori Energy-to-Power FIR Filter 368
8.8.2 Energy-to-Power FIR Predictor 370
8.9 Recursive Computation of Robust FIR Estimates 371
8.9.1 Uncontrolled Processes 372
8.9.2 Controlled Processes 372
8.10 FIR Smoothing under Disturbances 374
8.11 Summary 374
8.12 Problems 376
9 Robust FIR State Estimation for Uncertain Systems 379
9.1 Extended Models for Uncertain Systems 380
9.2 The a posteriori H2 FIR Filtering 386
9.2.1 H2-OFIR Filter 387
9.2.2 Bias-constrained H2-OFIR Filter 392
9.3 H2 FIR Prediction 394
9.3.1 Optimal H2 FIR Predictor 395
9.3.2 Bias-constrained H2-OUFIR Predictor 399
9.4 Suboptimal H2 FIR Structures using LMI 400
9.4.1 Suboptimal H2 FIR Filter 401
9.4.2 Bias-Constrained Suboptimal H2 FIR Filter 402
9.4.3 Suboptimal H2 FIR Predictor 403
9.4.4 Bias-Constrained Suboptimal H2 FIR Predictor 404
9.5 H8 FIR State Estimation for Uncertain Systems 405
9.5.1 The a posteriori H8 FIR Filter 405
9.5.2 H8 FIR Predictor 407
9.6 Hybrid H2{H8 FIR Structures 410
9.7 Generalized H2 FIR Structures for Uncertain Systems 411
9.7.1 The a posteriori L2-to-L8 FIR Filter 412
9.7.2 L2-to-L8 FIR Predictor 414
9.8 Robust L1 FIR Structures for Uncertain Systems 416
9.8.1 The a posteriori L8-to-L8 FIR Filter 417
9.8.2 L8-to-L8 FIR Predictor 417
9.9 Summary 418
9.10 Problems 419
10 Advanced Topics in FIR State Estimation 423
10.1 Distributed Filtering over Networks 423
10.1.1 Consensus in Measurements 424
10.1.2 Consensus in Estimates 429
10.2 Optimal Fusion Filtering under Correlated Noise 433
10.2.1 Error Covariances under Cross Correlation 436
10.3 Hybrid Kalman/UFIR Filter Structures 438
10.3.1 Fusing Estimates with Probabilistic Weights 438
10.3.2 Fusing Kalman and Weighted UFIR Estimates 442
10.4 Estimation under Delayed and Missing Data 444
10.4.1 Deterministic Delays and Missing Data 445
10.4.2 Randomly Delayed and Missing Data 449
10.5 Summary 453
10.6 Problems 454
11 Applications of FIR State Estimators 457
11.1 UFIR Filtering and Prediction of Clock States 458
11.1.1 Clock Model 458
11.1.2 Clock State Estimation over GPS-Based TIE Data 459
11.1.3 Master Clock Error Prediction 460
11.2 Suboptimal Clock Synchronization 463
11.2.1 Clock Digital Synchronization Loop 463
11.3 Localization over WSNs Using Particle/UFIR Filter 468
11.3.1 Sample Impoverishment Issue 470
11.3.2 Hybrid Particle/UFIR Filter 471
11.4 Self-localization over RFID Tag Grids 473
11.4.1 State Space Localization Problem 474
11.4.2 Localization Performance 476
11.5 INS/UWB-Based Quadrotor Localization 478
11.5.1 Quadrotor State Space Model under CMN 479
11.5.2 Localization Performance 481
11.6 Processing of Biosignals 481
11.6.1 ECG Signal Denoising using UFIR Smoothing 482
11.6.2 EMG Envelope Extraction using UFIR Filter 484
11.7 Summary 487
11.8 Problems 488
A Matrix Forms and Relationships 489
A.1 Derivatives 489
A.2 Matrix Identities 489
A.3 Special Matrices 490
A.4 Equations and Inequalities 491
A.5 Linear Matrix Inequalities 493
B Norms 495
B.1 Vector Norms 495
B.2 Matrix Norms 496
B.3 Signal Norms 497
B.4 System Norms 499
References 501
1.1 What is System State? 2
1.1.1 Why and How do We Estimate State? 2
1.1.2 What Model to Estimate State? 3
1.1.3 What are Basic State Estimates in Discrete Time? 5
1.2 Properties of State Estimators 6
1.2.1 Structures and Types 6
1.2.2 Optimality 10
1.2.3 Unbiased Optimality (Maximum Likelihood) 11
1.2.4 Suboptimality 14
1.2.5 Unbiasedness 17
1.2.6 Deadbeat 17
1.2.7 Denoising (Noise Power Gain) 17
1.2.8 Stability 18
1.2.9 Robustness 18
1.2.10 Computational Complexity 19
1.2.11 Memory Use 20
1.3 More About FIR State Estimators 20
1.4 Historical Overview and Most Noticeable Works 21
1.5 Summary 26
1.6 Problems 27
2 Probability and Stochastic Processes 31
2.1 Random Variables 31
2.1.1 Moments and Cumulants 33
2.1.2 Product Moments 39
2.1.3 Vector Random Variables 41
2.1.4 Conditional Probability. Bayes' Rule 42
2.1.5 Transformation of Random Variables 45
2.2 Stochastic Processes 47
2.2.1 Correlation Function 48
2.2.2 Power Spectral Density 51
2.2.3 Gaussian Processes 53
2.2.4 White Gaussian Noise 55
2.2.5 Markov Processes 57
2.3 Stochastic Differential Equation 60
2.3.1 Standard Stochastic Differential Equation 61
2.3.2 Ito and Stratonovich Stochastic Calculus 61
2.3.3 Diffusion Process Interpretation 62
2.3.4 Fokker-Planck-Kolmogorov Equation 63
2.3.5 Langevin Equation 64
2.4 Summary 65
2.5 Problems 66
3 State Estimation 71
3.1 Lineal Stochastic Process in State Space 71
3.1.1 Continuous-Time Model 73
3.1.2 Discrete-Time Model 77
3.2 Methods of Linear State Estimation 81
3.2.1 Bayesian Estimator 82
3.2.2 Maximum Likelihood Estimator 85
3.2.3 Least Squares Estimator 86
3.2.4 Unbiased Estimator 87
3.2.5 Kalman Filtering Algorithm 88
3.2.6 Backward Kalman Filter 94
3.2.7 Alternative Forms of Kalman Filter 96
3.2.8 General Kalman Filter 98
3.2.9 Kalman-Bucy Filter 110
3.3 Linear Recursive Smoothing 113
3.3.1 Rauch-Tung-Striebel Algorithm 113
3.3.2 Bryson-Frazier Algorithm 114
3.3.3 Two-Filter (Forward-Backward) Smoothing 115
3.4 Nonlinear Models and Estimators 116
3.4.1 Extended Kalman Filter 117
3.4.2 Unscented Kalman Filter 119
3.4.3 Particle Filtering 122
3.5 Robust State Estimation 126
3.5.1 Robustified Kalman Filter 127
3.5.2 Robust Kalman Filter 128
3.5.3 H8 Filtering 131
3.5.4 Game Theory H8 Filter 132
3.6 Summary 133
3.7 Problems 134
4 Optimal FIR and Limited Memory Filtering 139
4.1 Extended State-Space Model 140
4.2 The a posteriori Optimal FIR Filter 142
4.2.1 Batch Estimate and Error Covariance 143
4.2.2 Recursive Forms 145
4.2.3 System Identification 149
4.3 The a posteriori Optimal Unbiased FIR Filter 149
4.3.1 Batch OUFIR-I Estimate and Error Covariance 150
4.3.2 Recursive Forms for OUFIR-I Filter 151
4.3.3 Batch OUFIR-II Estimate and Error Covariance 153
4.3.4 Recursion Forms for OUFIR-II Filter 154
4.4 Maximum Likelihood FIR Estimator 158
4.4.1 ML-I FIR Filtering Estimate 158
4.4.2 Equivalence of ML-I FIR and OUFIR Filters 159
4.4.3 ML-II FIR Filtering Estimate 162
4.4.4 Properties of ML FIR State Estimators 163
4.5 The a priori FIR Filters 164
4.5.1 The a priori Optimal FIR Filter 164
4.5.2 The a priori Optimal Unbiased FIR Filter 165
4.6 Limited Memory Filtering 165
4.6.1 Batch Limited Memory Filter 166
4.6.2 Iterative LMF Algorithm using Recursions 168
4.7 Continuous-Time Optimal FIR Filter 169
4.7.1 Optimal Impulse Response 169
4.7.2 Differential Equation Form 171
4.8 Extended a posteriori OFIR Filtering 172
4.9 Properties of FIR State Estimators 174
4.10 Summary 179
4.11 Problems 182
5 Optimal FIR Smoothing 187
5.1 Introduction 187
5.2 Smoothing Problem 188
5.3 Forward Filter/Forward Model q-lag OFIR Smoothing 189
5.3.1 Batch Smoothing Estimate 190
5.3.2 Error Covariance 193
5.4 Backward OFIR Filtering 195
5.4.1 Backward State-Space Model 195
5.4.2 Batch Estimate 196
5.4.3 Recursive Estimate and Error Covariance 198
5.5 Backward Filter/Backward Model g-lag OFIR Smoother 202
5.5.1 Batch Smoothing Estimate 203
5.5.2 Error Covariance 204
5.6 Forward Filter/Backward Model q-Lag OFIR Smoother 205
5.6.1 Batch Smoothing Estimate 205
5.6.2 Error Covariance 208
5.7 Backward Filter/Forward Model q-Lag OFIR Smoother 208
5.7.1 Batch Smoothing Estimate 208
5.7.2 Error Covariance 211
5.8 Two-Filter q-lag OFIR Smoother 213
5.9 q-Lag ML FIR Smoothing 214
5.9.1 Batch q-lag ML FIR Estimate 215
5.9.2 Error Covariance 216
5.10 Summary 216
5.11 Problems 217
6 Unbiased FIR State Estimation 221
6.1 Introduction 221
6.2 The a posteriori UFIR Filter 222
6.2.1 Batch Form 222
6.2.2 Iterative Algorithm Using Recursions 224
6.2.3 Recursive Error Covariance 226
6.2.4 Optimal Averaging Horizon 228
6.3 Backward a posteriori UFIR Filter 234
6.3.1 Batch Form 235
6.3.2 Recursions and Iterative Algorithm 236
6.3.3 Recursive Error Covariance 239
6.4 The q-lag UFIR Smoother 240
6.4.1 Batch and Recursive Forms 240
6.4.2 Error Covariance 242
6.4.3 Equivalence of UFIR Smoothers 244
6.5 State Estimation using Polynomial Models 245
6.5.1 Problems Solved with UFIR Structures 246
6.5.2 The p-shift UFIR Filter 247
6.5.3 Filtering of Polynomial Models 250
6.5.4 Discrete Shmaliy Moments 252
6.5.5 Smoothing Filtering and Smoothing 252
6.5.6 Generalized Savitzky-Golay Filter 254
6.5.7 Predictive Filtering and Prediction 255
6.6 UFIR State Estimation under Colored Noise 256
6.6.1 Colored Measurement Noise 256
6.6.2 Colored Process Noise 259
6.7 Extended UFIR Filtering 262
6.7.1 First-Order Extended UFIR Filter 263
6.7.2 Second-Order Extended UFIR Filter 263
6.8 Robustness of UFIR Filter 266
6.8.1 Errors in Noise Covariances and Weighted Matrices 268
6.8.2 Model Errors 271
6.8.3 Temporary Uncertainties 274
6.9 Implementation of Polynomial UFIR Filters 276
6.9.1 Filter Structures in z-Domain 276
6.9.2 Transfer Function in DFT Domain 282
6.10 Summary 287
6.11 Problems 288
7 FIR Prediction and Receding Horizon Filtering 295
7.1 Introduction 295
7.2 Prediction Strategies 296
7.2.1 Kalman Predictor 296
7.3 Extended Predictive State-Space Model 298
7.4 UFIR Predictor 298
7.4.1 Batch UFIR Predictor 299
7.4.2 Iterative Algorithm using Recursions 299
7.4.3 Recursive Error Covariance 303
7.5 Optimal FIR Predictor 304
7.5.1 Batch Estimate and Error Covariance 305
7.5.2 Recursive Forms and Iterative Algorithm 306
7.6 Receding Horizon FIR Filtering 308
7.6.1 MVF-I Filter for Stationary Processes 309
7.6.2 MVF-II Filter for Nonstationary Processes 311
7.7 Maximum Likelihood FIR Predictor 313
7.7.1 ML-I FIR Predictor 314
7.7.2 ML-II FIR Predictor 315
7.8 Extended OFIR Prediction 315
7.9 Summary 317
7.10 Problems 318
8 Robust FIR State Estimation under Disturbances 323
8.1 Extended Models under Disturbances 324
8.2 The a posteriori H2 FIR Filtering 326
8.2.1 H2-OFIR Filter 328
8.2.2 Optimal Unbiased H2 FIR Filter 330
8.2.3 Suboptimal H2 FIR Filtering Algorithms 336
8.3 H2 FIR Prediction 338
8.3.1 H2-OFIR Predictor 339
8.3.2 Bias-constrained H2-OUFIR Predictor 341
8.3.3 Suboptimal H2 FIR Predictive Algorithms 341
8.3.4 Receding Horizon H2-MVF Filter 343
8.4 H8 FIR State Estimation 344
8.4.1 The a posteriori H8 FIR Filter 346
8.4.2 H8 FIR Predictor 350
8.5 H2{H8 FIR Filter and Predictor 354
8.6 Generalized H2 FIR State Estimation 355
8.6.1 Energy-to-Peak Lemma 355
8.6.2 L2-to-L8 FIR Filter and Predictor 359
8.7 L1 FIR State Estimation 362
8.7.1 Peak-to-Peak Lemma 363
8.7.2 L8-to-L8 FIR Filtering and Prediction 365
8.8 Game Theory FIR State Estimation 367
8.8.1 The a posteriori Energy-to-Power FIR Filter 368
8.8.2 Energy-to-Power FIR Predictor 370
8.9 Recursive Computation of Robust FIR Estimates 371
8.9.1 Uncontrolled Processes 372
8.9.2 Controlled Processes 372
8.10 FIR Smoothing under Disturbances 374
8.11 Summary 374
8.12 Problems 376
9 Robust FIR State Estimation for Uncertain Systems 379
9.1 Extended Models for Uncertain Systems 380
9.2 The a posteriori H2 FIR Filtering 386
9.2.1 H2-OFIR Filter 387
9.2.2 Bias-constrained H2-OFIR Filter 392
9.3 H2 FIR Prediction 394
9.3.1 Optimal H2 FIR Predictor 395
9.3.2 Bias-constrained H2-OUFIR Predictor 399
9.4 Suboptimal H2 FIR Structures using LMI 400
9.4.1 Suboptimal H2 FIR Filter 401
9.4.2 Bias-Constrained Suboptimal H2 FIR Filter 402
9.4.3 Suboptimal H2 FIR Predictor 403
9.4.4 Bias-Constrained Suboptimal H2 FIR Predictor 404
9.5 H8 FIR State Estimation for Uncertain Systems 405
9.5.1 The a posteriori H8 FIR Filter 405
9.5.2 H8 FIR Predictor 407
9.6 Hybrid H2{H8 FIR Structures 410
9.7 Generalized H2 FIR Structures for Uncertain Systems 411
9.7.1 The a posteriori L2-to-L8 FIR Filter 412
9.7.2 L2-to-L8 FIR Predictor 414
9.8 Robust L1 FIR Structures for Uncertain Systems 416
9.8.1 The a posteriori L8-to-L8 FIR Filter 417
9.8.2 L8-to-L8 FIR Predictor 417
9.9 Summary 418
9.10 Problems 419
10 Advanced Topics in FIR State Estimation 423
10.1 Distributed Filtering over Networks 423
10.1.1 Consensus in Measurements 424
10.1.2 Consensus in Estimates 429
10.2 Optimal Fusion Filtering under Correlated Noise 433
10.2.1 Error Covariances under Cross Correlation 436
10.3 Hybrid Kalman/UFIR Filter Structures 438
10.3.1 Fusing Estimates with Probabilistic Weights 438
10.3.2 Fusing Kalman and Weighted UFIR Estimates 442
10.4 Estimation under Delayed and Missing Data 444
10.4.1 Deterministic Delays and Missing Data 445
10.4.2 Randomly Delayed and Missing Data 449
10.5 Summary 453
10.6 Problems 454
11 Applications of FIR State Estimators 457
11.1 UFIR Filtering and Prediction of Clock States 458
11.1.1 Clock Model 458
11.1.2 Clock State Estimation over GPS-Based TIE Data 459
11.1.3 Master Clock Error Prediction 460
11.2 Suboptimal Clock Synchronization 463
11.2.1 Clock Digital Synchronization Loop 463
11.3 Localization over WSNs Using Particle/UFIR Filter 468
11.3.1 Sample Impoverishment Issue 470
11.3.2 Hybrid Particle/UFIR Filter 471
11.4 Self-localization over RFID Tag Grids 473
11.4.1 State Space Localization Problem 474
11.4.2 Localization Performance 476
11.5 INS/UWB-Based Quadrotor Localization 478
11.5.1 Quadrotor State Space Model under CMN 479
11.5.2 Localization Performance 481
11.6 Processing of Biosignals 481
11.6.1 ECG Signal Denoising using UFIR Smoothing 482
11.6.2 EMG Envelope Extraction using UFIR Filter 484
11.7 Summary 487
11.8 Problems 488
A Matrix Forms and Relationships 489
A.1 Derivatives 489
A.2 Matrix Identities 489
A.3 Special Matrices 490
A.4 Equations and Inequalities 491
A.5 Linear Matrix Inequalities 493
B Norms 495
B.1 Vector Norms 495
B.2 Matrix Norms 496
B.3 Signal Norms 497
B.4 System Norms 499
References 501