王大轶，研究员，现任中国空间技术研究院总体部副部长，中国宇航学会英文刊Advances in Astronautics Science and Technology（《航天科技前沿》）编委，国家杰出青年科学基金获得者，国防科技卓越青年科学基金获得者，国家万人计划科技创新领军人才，“973项目”技术首席专家。在航天器自主导航与控制领域进行创新研究工作，解决了一系列关键技术问题，为嫦娥月球探测器等型号飞行试验成功做出了贡献。2016年获何梁何利基金科学与技术创新奖，2017年入选国家级百千万人才工程，是国务院政府特殊津贴专家、国家有突出贡献中青年专家。获国家技术发明二等奖1项，部级一等奖4项、二等奖4项。
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
3 Estimation Fusion Algorithm
3.1 Linear Fusion Models and Algorithms
3.1.1 Linear Unified Model
3.1.2 Fusion Algorithm from the Linear Unified Model
3.1.3 Covariance Intersection Algorithm in the Distributed Fusion
3.2 Centralized-Fusion Kalman Filtering for a Dynamic System
3.2.1 Parallel Filtering
3.2.2 Sequential Filtering
3.2.3 Data Compression Filtering
3.3 Distributed-Fusion Kalman Filtering for a Dynamic System
3.3.1 Standard Distributed Kalman Filtering
3.3.2 Covariance Intersection Algorithm
3.3.3 Federated Filtering Algorithm
3.4 Brief Summary
References
4 Performance Analysis
4.1 Observability of Linear System
4.1.1 Observability Analysis of LTI Systems
4.1.2 Observability Analysis of LTV Systems
4.2 Observability of Nonlinear Systems
4.2.1 Definition and Criteria of the Observability of Nonlinear Systems
4.2.2 Observability Analysis Based on Singular Value Decomposition
4.3 Degree of Observability for Autonomous Navigation System
4.3.1 Observability Gramian Based Method
4.3.2 Error Covariance-Based Method
4.4 Monte Carlo Method
4.5 Technique of Linear Covariance Analysis
4.6 Brief Summary
References
5 Time and Coordinate Systems
5.1 Time Systems
5.1.1 Definition of Time System
5.1.2 Definition and Conversion of Julian Date
5.2 Coordinate Frames
5.2.1 Definition of Reference Coordinate System
5.2.2 Coordinate Transformation
5.3 Ephemeris of Navigational Celestial Bodies
5.3.1 Calculation of High-Precision Celestial Ephemerides
5.3.2 Calculation of Simple Celestial Ephemerides
5.4 Brief Summary
References
6 Dynamic Models and Environment Models
6.1 Orbit Dynamics Model
6.1.1 Orbital Perturbation Model
6.1.2 Spacecraft Orbit Dynamics Model
6.2 Attitude Kinematics Model
6.2.1 Description of Attitude
6.2.2 Attitude Kinematics Equation
6.3 Mars Environment Model
6.3.1 Mars Ellipsoid Model
6.3.2 Mars Gravitation Field Model
6.4 Asteroid Environment Model
6.4.1 Asteroid 3D Model
6.4.2 Asteroid Gravitation Field Model
6.5 Brief Summary
References
7 Inertial Autonomous Navigation Technology
7.1 Measurement Equation
7.1.1 Gyroscope Measurement Equation
7.1.2 Accelerometer Measurement Equation
7.2 Differential Equation of Strapdown Inertial Navigation .
7.3 Strapdown Inertial Navigation Update Equations
7.3.1 Attitude Update Equation
7.3.2 Inertial Velocity Update Equation
7.3.3 Inertial Position Update Equation
7.4 Com
