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開本:128開 紙張:膠版紙 包裝:平裝-膠訂 是否套裝:否 國際標準書號ISBN:9787030635471 作者:馬宏賓 出版社:科學出版社 出版時間:2020年03月 
" 目錄 Contents Part I Kalman Filtering: Preliminaries 1 Introduction to Kalman Filtering 3 1.1 What Is Filtering? 3 1.2 Historical Remarks 5 1.3 Wiener Filter 7 1.4 Kalman Filter 7 1.5 Conclusion 9 References 9 2 Challenges in Kalman Filtering11 2.1 Standard Kalman Filter 11 2.2 Requirements of Standard Kalman Filtering 14 2.3 Effects of System Uncertainties 15 2.4 Effects of Multiple Sensors 16Contents Part I Kalman Filtering: Preliminaries 1 Introduction to Kalman Filtering 3 1.1 What Is Filtering? 3 1.2 Historical Remarks 5 1.3 Wiener Filter 7 1.4 Kalman Filter 7 1.5 Conclusion 9 References 9 2 Challenges in Kalman Filtering11 2.1 Standard Kalman Filter 11 2.2 Requirements of Standard Kalman Filtering 14 2.3 Effects of System Uncertainties 15 2.4 Effects of Multiple Sensors 16 2.5 Effects of System Couplings 16 2.6 Conclusion 17 References 17 Part II Kalman Filtering for Uncertain Systems 3 Kalman Filter with Recursive Process Noise Covariance Estimation 21 3.1 Introduction 21 3.2 Problem Formulation 23 3.2.1 Standard Kalman Filter 23 3.2.2 Problem To Be Resolved 23 3.3 Basic Idea: Estimating Covariance Matrix 26 3.4 Kalman Filter Based on Algorithm RecursiveCovarianceEstimation 31 3.5 Stability Analysis 33 3.6 Simulations 41 3.6.1 One-Dimensional Simulation 41 3.6.2 Multidimensional Simulation 42 3.6.3 Integrated Navigations Simulation 43 3.7 Conclusion 46 References 48 4 Kalman Filter with Recursive Covariance Estimation Revisited with Technical Conditions Reduced 51 4.1 Introduction 51 4.2 Problem Formulation 53 4.3 Kalman Filter with Recursive Covariance Estimation 56 4.3.1 Basic Method: Covariance Matrix Estimation 56 4.3.2 KF-RCE Algorithm for LTI Systems 58 4.4 Stability Analysis 60 4.5 Simulation Experiments 65 4.6 Conclusion 68 References 68 5 Modified Kalman Filter with Recursive Covariance Estimation for Gyroscope Denoising 71 5.1 Introduction 71 5.2 Problem Formulation 73 5.2.1 Kalman Filter 73 5.2.2 Problem to Be Resolved 74 5.3 Modified Kalman Filter with Recursive Covariance Matrix 76 5.3.1 Basic Idea: Estimating Covariance Matrix 76 5.3.2 Modified Kalman Filter with Recursive Covariance Matrix 77 5.3.3 Stability Analysis 79 5.3.4 Simulation Study 86 5.4 Experimental Tests 87 5.5 Conclusion 93 References 93 6 Real-Time State Estimator Without Noise Covariance Matrices Knowledge 95 6.1 Introduction 95 6.2 Problem Formulation 97 6.3 The Fast Minimum Norm Filtering Algorithm 99 6.3.1 Time Update 100 6.3.2 Measurement Update 100 6.4 Numerical Examples 106 6.4.1 Example I: Measurement Feedback Simulation 107 6.4.2 Example II: Data Fusion Simulation 107 6.4.3 Example III: Integrated Navigation Simulation 115 6.5 Conclusion 115 References 118 7 A Framework of Finite-Model Kalman Filter with Case Study: MVDP-FMKF Algorithm 119 7.1 Introduction 119 7.2 Kalman Filter 121 7.3 Framework of Finite-Model Kalman Filter 122 7.4 MVDP Finite-Model Kalman Filter Algorithm 125 7.4.1 Derivation of di 126 7.4.2 Two-Model MVDP-FMKF Algorithm 131 7.4.3 General MVDP-FMKF Algorithm 134 7.5 Simulation of the MVDP-FMKF Algorithm 136 7.5.1 One-Dimensional Simulation 137 7.5.2 Multidimensional Simulation 142 7.6 Experimental Test 143 7.7 Conclusion 144 References 145 8 Kalman Filters for Continuous Parametric Uncertain Systems 147 8.1 Introduction 147 8.2 Problem Formulation 149 8.3 The Estimation Algorithm 150 8.3.1 The Kalman Filtering-Based Parameter Estimation 150 8.3.2 The Kalman Filtering-Based State Estimation 153 8.4 Convergence Analysis 156 8.5 Numerical Example 158 8.6 Conclusions 160 References 160 Part III Kalman Filtering for Multi-sensor Systems 9 Optimal Centralized, Recursive, and Distributed Fusion for Stochastic Systems with Coupled Noises 165 9.1 Introduction 165 9.2 Problem Formulation 166 9.3 Optimal Fusion Algorithms 167 9.4 Performance Analysis and Computer Simulation 182 9.5 Summary 196 References 197 10 Optimal Estimation for Multirate Systems with Unreliable Measurements and Correlated Noise 199 10.1 Problem Formulations 201 10.2 Optimal Distributed Fusion Algorithm 203 10.2.1 Local State Estimation with Normal Measurements 203 10.2.2 Local State Estimation with Unreliable Measurements 206 10.2.3 Optimal Distributed Fusion Estimation with Unreliable Measurements 208 10.3 Numerical Example 214 10.4 Summary 220 References 220 11 CKF-Based State Estimation of Nonlinear System by Fusion of Multirate Multisensor Unreliable Measurements 223 11.1 Introduction 223 11.2 Problem Formulation 225 11.3 Multirate Multisensor Data Fusion Algorithm 225 11.4 Numerical Simulation 230 11.4.1 Simple Example on Tracking of a Ship 230 11.4.2 Target Tracking on Aircraft 234 11.5 Summary 236 References 237 Part IV Kalman Filtering for Multi-agent Systems 12 Decentralized Adaptive Filtering for Multi-agent Systems with Uncertain Couplings 241 12.1 Introduction 241 12.2 Problem Statement 243 12.2.1 Model 1: Linear Model with Output Coupling 243 12.2.2 Model 2: Linear Model with State Coupling 244 12.2.3 Model 3: Nonlinear Model with Output Couplin | | | | | |