卡尔曼滤波与信息融合

ContentsPart I Kalman Filtering: Preliminaries1 Introduction to Kalman Filtering 31.1 What Is Filtering? 31.2 Historical Remarks 51.3 Wiener Filter 71.4 Kalman Filter 71.5 Conclusion 9References 92 Challenges in Kalman Filtering112.1 Standard Kalman Filter 112.2 Requirements of Standard Kalman Filtering 142.3 Effects of System Uncertainties 152.4 Effects of Multiple Sensors 162.5 Effects of System Couplings 162.6 Conclusion 17References 17Part II Kalman Filtering for Uncertain Systems3 Kalman Filter with Recursive Process Noise Covariance Estimation 213.1 Introduction 213.2 Problem Formulation 233.2.1 Standard Kalman Filter 233.2.2 Problem To Be Resolved 233.3 Basic Idea: Estimating Covariance Matrix 263.4 Kalman Filter Based on Algorithm RecursiveCovarianceEstimation 313.5 Stability Analysis 333.6 Simulations 413.6.1 One-Dimensional Simulation 413.6.2 Multidimensional Simulation 423.6.3 Integrated Navigations Simulation 433.7 Conclusion 46References 484 Kalman Filter with Recursive Covariance Estimation Revisited with Technical Conditions Reduced 514.1 Introduction 514.2 Problem Formulation 534.3 Kalman Filter with Recursive Covariance Estimation 564.3.1 Basic Method: Covariance Matrix Estimation 564.3.2 KF-RCE Algorithm for LTI Systems 584.4 Stability Analysis 604.5 Simulation Experiments 654.6 Conclusion 68References 685 Modified Kalman Filter with Recursive Covariance Estimation for Gyroscope Denoising 715.1 Introduction 715.2 Problem Formulation 735.2.1 Kalman Filter 735.2.2 Problem to Be Resolved 745.3 Modified Kalman Filter with Recursive Covariance Matrix 765.3.1 Basic Idea: Estimating Covariance Matrix 765.3.2 Modified Kalman Filter with Recursive Covariance Matrix 775.3.3 Stability Analysis 795.3.4 Simulation Study 865.4 Experimental Tests 875.5 Conclusion 93References 936 Real-Time State Estimator Without Noise Covariance Matrices Knowledge 956.1 Introduction 956.2 Problem Formulation 976.3 The Fast Minimum Norm Filtering Algorithm 996.3.1 Time Update 1006.3.2 Measurement Update 1006.4 Numerical Examples 1066.4.1 Example I: Measurement Feedback Simulation 1076.4.2 Example II: Data Fusion Simulation 1076.4.3 Example III: Integrated Navigation Simulation 1156.5 Conclusion 115References 1187 A Framework of Finite-Model Kalman Filter with Case Study: MVDP-FMKF Algorithm 1197.1 Introduction 1197.2 Kalman Filter 1217.3 Framework of Finite-Model Kalman Filter 1227.4 MVDP Finite-Model Kalman Filter Algorithm 1257.4.1 Derivation of di 1267.4.2 Two-Model MVDP-FMKF Algorithm 1317.4.3 General MVDP-FMKF Algorithm 1347.5 Simulation of the MVDP-FMKF Algorithm 1367.5.1 One-Dimensional Simulation 1377.5.2 Multidimensional Simulation 1427.6 Experimental Test 1437.7 Conclusion 144References 1458 Kalman Filters for Continuous Parametric Uncertain Systems 1478.1 Introduction 1478.2 Problem Formulation 1498.3 The Estimation Algorithm 1508.3.1 The Kalman Filtering-Based Parameter Estimation 1508.3.2 The Kalman Filtering-Based State Estimation 1538.4 Convergence Analysis 1568.5 Numerical Example 1588.6 Conclusions 160References 160Part III Kalman Filtering for Multi-sensor Systems9 Optimal Centralized， Recursive， and Distributed Fusion for Stochastic Systems with Coupled Noises 1659.1 Introduction 1659.2 Problem Formulation 1669.3 Optimal Fusion Algorithms 1679.4 Performance Analysis and Computer Simulation 1829.5 Summary 196References 19710 Optimal Estimation for Multirate Systems with Unreliable Measurements and Correlated Noise 19910.1 Problem Formulations 20110.2 Optimal Distributed Fusion Algorithm 20310.2.1 Local State Estimation with Normal Measurements 20310.2.2 Local State Estimation with Unreliable Measurements 20610.2.3 Optimal Distributed Fusion Estimation with Unreliable Measurements 20810.3 Numerical Example 21410.4 Summary 220References 22011 CKF-Based State Estimation of Nonlinear System by Fusion of Multirate Multisensor Unreliable Measurements 22311.1 Introduction 22311.2 Problem Formulation 22511.3 Multirate Multisensor Data Fusion Algorithm 22511.4 Numerical Simulation 23011.4.1 Simple Example on Tracking of a Ship 23011.4.2 Target Tracking on Aircraft 23411.5 Summary 236References 237Part IV Kalman Filtering for Multi-agent Systems12 Decentralized Adaptive Filtering for Multi-agent Systems with Uncertain Couplings 24112.1 Introduction 24112.2 Problem Statement 24312.2.1 Model 1: Linear Model with Output Coupling 24312.2.2 Model 2: Linear Model with State Coupling 24412.2.3 Model 3: Nonlinear Model with Output Couplin