Robust Kalman Filtering for Signals and Systems with Large Uncertainties

"One of the most significant ideas to emerge in the modern control era is the Kalman Filter. It has had wide application in stochastic control, fault diagnosis, process control, channel equalization, sensor data fusion, and other areas of engineering. The purpose of this new book is to present recent developments in the theory of robust-state estimation for the case in which a process model contains significant uncertainties and nonlinearities. In particular, the book looks at the various ways in which the standard Kalman Filter can be modified to make it robust against large parameter uncertainties. Most of the work concentrates on the case of linear uncertain systems and robust filters constructed via Riccati equation methods. This approach extends the classic Kalman Filter to the realm of systems with uncertain parameters, with extensions of the linear theory to the case of nonlinear uncertain systems. In addition to coverage of standard filtering problems, more general filter problems are introduced, such as robust filters with missing data, design of low-order filters, robust prediction, and others ... The book is an essential text/reference for graduates, researchers, and professionals in electrical, mechanical, and control engineering, applied mathematics, and computer engineering. All scientists and engineers engaged in robust control and filtering theory research will find the book a useful resource"--Back cover.