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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.

Inhaltsverzeichnis

Frontmatter

Chapter 1. Introduction

Abstract
Navigation generally refers to the determination of the orbit (position and velocity) and attitude parameters of a vehicle (or a moving body) relative to a coordinate system at a given time. In general, spacecraft navigation refers only to the determination of orbit parameters. In general, the navigation relying on only the onboard measuring and computing devices rather than ground support is referred to as spacecraft autonomous navigation, while the spacecraft autonomous navigation realized by the fusion processing of multiple information sources (multiple observed objects, multi-sensor measurements, priori knowledge, etc.) is called multi-source information fusion-based autonomous navigation. Equation Chapter (Next) Section 1.
Dayi Wang, Maodeng Li, Xiangyu Huang, Xiaowen Zhang

Chapter 2. Point Estimation Theory

Abstract
Estimation theory is a branch of statistics that deals with estimating interested parameters from random observations (or sampled data). An estimator (also called estimation rules), defining the corresponding rules of inference, can be classified into point and interval estimators. A point estimator gives an approximate value of unknown parameters, whereas an interval estimator calculates an interval that covers the unknown parameters with a high probability. Navigation estimation is often the issue of point estimation, so this chapter only focuses on the relevant contents of point estimation.
Dayi Wang, Maodeng Li, Xiangyu Huang, Xiaowen Zhang

Chapter 3. Estimation Fusion Algorithm

Abstract
Multiple source information-based autonomous navigation is essentially an estimation fusion problem. This chapter will go deep into the estimation fusion algorithm. Section 3.1 presents linear models and algorithms, mainly including linear unified model and its fusion algorithm as well as covariance intersection algorithm in the distributed fusion, which can be used to estimate the parameters of a static system and states of a dynamic system. Sections 3.2 and 3.3 describe the centralized Kalman filter and distributed Kalman filter of a dynamic system, respectively, and discuss several implementation models of centralized filtering and typical models of distributed filtering.
Dayi Wang, Maodeng Li, Xiangyu Huang, Xiaowen Zhang

Chapter 4. Performance Analysis

Abstract
The main analysis methods of system performance include observability analysis, Monte Carlo method, and linear covariance analysis. This chapter starts with the basic concept of observability to present the definition and observability criteria of linear and nonlinear systems, mainly introducing observability analysis methods of nonlinear systems. In the analysis of system observability, methods based on singular value decomposition, observability Gramian, and estimation error covariance are taken as examples for discussion. Then, the definition, implementation method, and general structure of Monte Carlo method are briefly introduced. Finally, the linear covariance technique is introduced, and the general method of establishing the linear propagation formula of error covariance is described for onboard autonomous navigation system.
Dayi Wang, Maodeng Li, Xiangyu Huang, Xiaowen Zhang

Chapter 5. Time and Coordinate Systems

Abstract
Time and coordinate systems are two basic elements for navigation system. For a navigation system, the position and velocity of a spacecraft should be described in a special time and coordinate system. In addition, the establishment of orbit dynamics model and the generation of navigation measurement data often involve the calculation of celestial ephemeris. This chapter presents the description of time system, definitions, and transformation of reference coordinate systems, and ephemeris calculation for navigation celestial bodies.
Dayi Wang, Maodeng Li, Xiangyu Huang, Xiaowen Zhang

Chapter 6. Dynamic Models and Environment Models

Abstract
In the design and performance evaluation of a navigation system, the orbit dynamics model, attitude kinematics model, and environment model should be established. This chapter first addresses orbital perturbation model, establishes orbit dynamic equations, and then elaborates on four typical parametric forms for attitude description, the conversion relations between attitude parameters, and the attitude kinematics model corresponding to each parameter. Finally, Mars and asteroids are taken as examples to introduce the modeling methods of celestial environment, including celestial body shape modeling and gravitational field modeling.
Dayi Wang, Maodeng Li, Xiangyu Huang, Xiaowen Zhang

Chapter 7. Inertial Autonomous Navigation Technology

Abstract
In this chapter, the measurement equations of gyroscope and accelerometer and the differential equations of strapdown inertial navigation system (SINS) are presented at first. For high accurate application, in order to reduce the influence of output noise on the calculation accuracy of the system and make full use of the output information, the outputs of gyroscope and accelerometer are all increments, namely the angle increment output by gyroscope and the velocity increment output by accelerometer.
Dayi Wang, Maodeng Li, Xiangyu Huang, Xiaowen Zhang

Chapter 8. Optical Autonomous Navigation Technology

Abstract
This chapter presents optical autonomous navigation technology. The principles of optical autonomous navigation and optical imaging sensors are introduced in 8.1 and Sect. 8.2, respectively.
Dayi Wang, Maodeng Li, Xiangyu Huang, Xiaowen Zhang

Chapter 9. Optical/Pulsar Integrated Autonomous Navigation Technology

Abstract
In this chapter, the fusion of optical autonomous navigation and pulsar-based autonomous navigation will be studied. The former can provide priori state information to the latter, while the latter can enhance the former’s performance. Sections 9.1 and 9.2 briefly review measurement equations of optical autonomous navigation and pulsar-based autonomous navigation, respectively. Section 9.3 studies more deeply the navigation beacon planning algorithm. For the integrated navigation system fused on a single optical beacon and pulsar, the optimal beacons configuration is investigated based on geometric observability analysis. For a general optical/pulsar fused navigation system, the onboard planning algorithm of navigation beacons based on dynamic observability analysis is also studied. Section 9.4 presents optical/pulsar integrated navigation filtering. In Sect. 9.5, numerical simulation of the optical/pulsar integrated autonomous navigation technology for deep-space transfer, approaching, and surrounding phases are provided.
Dayi Wang, Maodeng Li, Xiangyu Huang, Xiaowen Zhang

Chapter 10. Altimeter and Velocimeter-/Optical-Aided Inertial Navigation Technology

Abstract
Inertial navigation has been successfully applied in soft landing missions. The inertial navigation error is gradually increased caused by initial navigation error and inertial measurement unit (IMU) measurement error. In order to improve the navigation accuracy of the lander relative to the celestial body surface, direct measurements of the lander relative to the celestial body surface are usually used to correct inertial navigation results. Candidate sensors include altimeter, velocimeter, and optical imaging sensor. This chapter focuses on the altimeter-/velocimeter-aided inertial navigation and optical-aided inertial navigation for soft landing missions.
Dayi Wang, Maodeng Li, Xiangyu Huang, Xiaowen Zhang

Chapter 11. Simulation Testing Techniques for Autonomous Navigation Based on Multi-source Information Fusion

Abstract
This chapter introduces semi-physical ground test techniques of the autonomous navigation based on multi-source information fusion, including scheme design, system composition and test cases. Two semi-physical test facilities will be presented: one is optical and pulsar integrated autonomous navigation test facility for the transfer, approaching and orbiting phases of deep space exploration missions, and the other is altimeter and velocimeter aided inertial navigation system (INS) test facility for the hovering, obstacle avoidance and slow descent phases of lunar soft landing missions.
Dayi Wang, Maodeng Li, Xiangyu Huang, Xiaowen Zhang

Chapter 12. Prospect for Multi-source Information Fusion Navigation

Abstract
Autonomous navigation is the core technology of spacecraft autonomous operation, but a single navigation mode cannot meet the requirements for long-time operation, high accuracy, and high reliability. The autonomous navigation technique based on multi-source information fusion can realize the advantage complementation among subsystems and achieve the performance better than that of subsystems. Therefore, it is an effective way to improve the performance of autonomous navigation.
Dayi Wang, Maodeng Li, Xiangyu Huang, Xiaowen Zhang

Backmatter

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