This book 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 relationships between 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.
目录
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
