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| 內容簡介： |
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Thi□ □ook introduces readers to the fundamentals of estimation and dynamical system theory， and their applications in the field of multi-source information fused autonomous navigation for spacecraft. The content is divided into two parts： theory and application. The theory part （Part I） covers the mathematical background of navigation algorithm design， including parameter and state estimate methods， linear fusion， centralized and distributed fusion， observability analysis， Monte Carlo technology， and linear covariance analysis. In turn， the application part （Part II） focuses on autonomous navigation algorithm design for different phases of deep space missions， which involves multiple sensors， such as inertial measurement units， optical image sensors， and pulsar detectors. By concentrating on the relationship□ □etween estimation theory and autonomous navigation systems for spacecraft， the book bridges the gap between theory and practice. A wealth of helpful formulas and various types of estimators are also included to help readers grasp basic estimation concepts and offer them a ready reference guide.
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| 目錄：
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1 Introduction
1．1 Autonomous Navigation Technology
1．1．1 Inertial Navigation
1．1．2 Autonomous Optical Navigation
1．1．3 Autonomous Pulsar-Based Navigation
1．2 Multi-source Information Fusion Technology
1．2．1 Definition of Multi-source Information Fusion
1．2．2 Classification of Multi-source Information FusionTechnologie2<2r>
1．2．3 Multi-source Information Fusion Method2<2r>
1．3 Autonomous Navigation Technology Based onMulti-source Information Fusion
1．3．1 Research and Application Progres2<2r>1．3．2 Necessity and Advantage2<2r>1．4 Outline
Reference2<2r>
2Point Estimation Theory
2．1 Basic Concept2<2r>2．2 Common Parameter Estimator2<2r>
2．2．1 MMSE Estimation
2．2．2 ML Estimator
2．2．3 Maximum a Posteriori （MAP） Estimator
2．2．4 Weight Least-Square （WLS） Estimator
2．3 Closed Form Parameter Estimator2<2r>
2．3．1 Linear Estimator
2．3．2 MMSE Estimator for Jointly Gaussian Distribution
2．3．3 Estimation Algorithms for Linear Measurement Equation
2．4 State Estimation Algorithms in Dynamic System2<2r>
2．4．1 Recursive Bayesian Estimation
2．4．2 Kalman Filtering
2．4．3 Extended Kalman Filtering
2．4．4 Unscented Kalman Filtering
2．4．5 Constrained Kalman Filtering
2．5 Brief Summary
Reference2<2r>……
3Estimation Fusion Algorithm
4Performance Analysi2<2r>5 Time and Coordinate System2<2r>6 DynanucModels and Environment Model2<2r>7 Inertial Autonomous NavigationTechnology
8Optical Autonomous Navigation Technology
9Optical/Pulsar Integrated Autonomous Navigation Technology
10Altimeter and Velocimeter-/Optical-Aided Inertial Navigation Technology
11Simulation Testing Techniques for Autonomous Navigation Based on Multi-sourceInformation Fusion
12Prospect for Multi-source Information Fusion Navigation
Appendix
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