航天器多源信息融合自主导航技术=Spacecraft Autonomous

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 Fusion Technologies
1．2．3 Multi-source Information Fusion Methods
1．3 Autonomous Navigation Technology Based on Multi-source Information Fusion
1．3．1 Research and Application Progress
1．3．2 Necessity and Advantages
1．4 Outline
References

2 Point Estimation Theory
2．1 Basic Concepts
2．2 Common Parameter Estimators
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 Estimators
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 Systems
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
References
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3 Estimation Fusion Algorithm
4 Performance Analysis
5 Time and Coordinate Systems
6 Dynanuc Models and Environment Models
7 Inertial Autonomous Navigation Technology
8 Optical Autonomous Navigation Technology
9 Optical/Pulsar Integrated Autonomous Navigation Technology
10 Altimeter and Velocimeter-/Optical-Aided Inertial Navigation Technology
11 Simulation Testing Techniques for Autonomous Navigation Based on Multi-source Information Fusion
12 Prospect for Multi-source Information Fusion Navigation

Appendix