This book illustrates the usefulness of estimation in engineering and science. It uses dynamic models to provide immediate results of estimation concepts with minimal reliance on mathematics. This second edition discusses a number of new topics, including higher order nonlinear filters, inertial navigation, and nonlinear stochastic processes. The authors cover prototype algorithms to stimulate the development and intelligent use of efficient computer programs. MATLAB is used throughout, with the code on a supporting website. In the appendices, the authors review statistics, optimization, probability, and matrix analysis.…mehr
This book illustrates the usefulness of estimation in engineering and science. It uses dynamic models to provide immediate results of estimation concepts with minimal reliance on mathematics. This second edition discusses a number of new topics, including higher order nonlinear filters, inertial navigation, and nonlinear stochastic processes. The authors cover prototype algorithms to stimulate the development and intelligent use of efficient computer programs. MATLAB is used throughout, with the code on a supporting website. In the appendices, the authors review statistics, optimization, probability, and matrix analysis.
John L. Crassidis, Ph.D., is a professor of mechanical and aerospace engineering and the associate director of the Center for Multisource Information Fusion at the University at Buffalo, State University of New York. He previously worked at Texas A&M University, the Catholic University of America, and NASA's Goddard Space Flight Center, where he contributed to attitude determination and control schemes for numerous spacecraft missions. John L. Junkins, Ph.D., is a distinguished professor of aerospace engineering and the founder and director of the Center for Mechanics and Control at Texas A&M University. In addition to his historical contributions in analytical dynamics and spacecraft GNC, Dr. Junkins and his team have designed, developed, and demonstrated several new electro-optical sensing technologies.
Inhaltsangabe
Least Squares Approximation. Probability Concepts in Least Squares. Sequential State Estimation. Advanced Topics in Sequential State Estimation. Batch State Estimation. Parameter Estimation: Applications. Estimation of Dynamic Systems: Applications. Optimal Control and Estimation Theory. Appendices. Index.
Least Squares Approximation. Probability Concepts in Least Squares. Sequential State Estimation. Advanced Topics in Sequential State Estimation. Batch State Estimation. Parameter Estimation: Applications. Estimation of Dynamic Systems: Applications. Optimal Control and Estimation Theory. Appendices. Index.
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