Описание: Discover the utility of the smooth variable structure filter with this insightful and powerful new resource
Nonlinear Filters: Theory and Applications delivers an insightful explanation of a new state estimation strategy, called the smooth variable structure filter (SVSF), designed to fill the gap between the literature on nonlinear filters and nonlinear observers. The book offers researchers outside of the control society a valuable resource on nonlinear observers.
The SVSF is a predictor-corrector estimator formulated based on a stability theorem, used to confine the estimated states within a neighborhood of their true values. It can improve performance in the presence of severe and changing modeling uncertainties and noise. The SVSF provides a set of secondary performance indicators that pertain to each estimate, which allows for the dynamic refinement of the filter model.
Readers of Nonlinear Filters will greatly benefit from the SVSFs robust stability and its capability to compensate for uncertainties that are abruptly introduced to a system. Readers will also enjoy:
- A companion website that provides mathematical models, software code, and experimental data
- The inclusion of benchmark problems in nonlinear estimation that help to standardize filter performance evaluation
- Organization that allows the book to act as a stand-alone, self-contained reference
- Theoretical work supported by included computer simulations in MATLAB
- A thorough exploration of the theory of nonlinear filtering that bridges the gap between different science and engineering disciplines
Perfect for researchers, professors, and graduate students in electrical, mechanical, chemical, and biomedical engineering, Nonlinear Filters: Theory and Applications will also earn a place in the libraries of those studying or practicing in fields involving sensor networks, data fusion, navigation and tracking, applied mathematics, economics, and robotics.
