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Spacecraft Autonomous Navigation Technologies Based on Multi-source Information Fusion (Paperback, 1st ed. 2021)
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Spacecraft Autonomous Navigation Technologies Based on Multi-source Information Fusion (Paperback, 1st ed. 2021)
Series: Space Science and Technologies
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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.
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