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Spacecraft Autonomous Navigation Technologies Based on Multi-source Information Fusion (Hardcover, 1st ed. 2021): Dayi Wang,... Spacecraft Autonomous Navigation Technologies Based on Multi-source Information Fusion (Hardcover, 1st ed. 2021)
Dayi Wang, Maodeng Li, Xiangyu Huang, Xiaowen Zhang
R5,877 Discovery Miles 58 770 Ships in 12 - 17 working days

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.       

Spacecraft Autonomous Navigation Technologies Based on Multi-source Information Fusion (Paperback, 1st ed. 2021): Dayi Wang,... Spacecraft Autonomous Navigation Technologies Based on Multi-source Information Fusion (Paperback, 1st ed. 2021)
Dayi Wang, Maodeng Li, Xiangyu Huang, Xiaowen Zhang
R6,167 Discovery Miles 61 670 Ships in 10 - 15 working days

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.

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