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Nonlinear Estimation - Methods and Applications with Deterministic Sample Points (Hardcover)
Loot Price: R3,657
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Nonlinear Estimation - Methods and Applications with Deterministic Sample Points (Hardcover)
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Nonlinear Estimation: Methods and Applications with Deterministic
Sample Points focusses on a comprehensive treatment of
deterministic sample point filters (also called Gaussian filters)
and their variants for nonlinear estimation problems, for which no
closed-form solution is available in general. Gaussian filters are
becoming popular with the designers due to their ease of
implementation and real time execution even on inexpensive or
legacy hardware. The main purpose of the book is to educate the
reader about a variety of available nonlinear estimation methods so
that the reader can choose the right method for a real life
problem, adapt or modify it where necessary and implement it. The
book can also serve as a core graduate text for a course on state
estimation. The book starts from the basic conceptual solution of a
nonlinear estimation problem and provides an in depth coverage of
(i) various Gaussian filters such as the unscented Kalman filter,
cubature and quadrature based filters, Gauss-Hermite filter and
their variants and (ii) Gaussian sum filter, in both discrete and
continuous-discrete domain. Further, a brief description of filters
for randomly delayed measurement and two case-studies are also
included. Features: The book covers all the important Gaussian
filters, including filters with randomly delayed measurements.
Numerical simulation examples with detailed matlab code are
provided for most algorithms so that beginners can verify their
understanding. Two real world case studies are included: (i)
underwater passive target tracking, (ii) ballistic target tracking.
The style of writing is suitable for engineers and scientists. The
material of the book is presented with the emphasis on key ideas,
underlying assumptions, algorithms, and properties. The book
combines rigorous mathematical treatment with matlab code,
algorithm listings, flow charts and detailed case studies to deepen
understanding.
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