Introduction To Random Signals Estimation Theory And Kalman Filtering

The techniques used for the extraction of information from received or ob served signals are applicable in many diverse areas such as radar, sonar, communications, geophysics, remote sensing, acoustics, meteorology, med ical imaging systems, and electronics warfare. The received signal is usually disturbed by thermal, electrical, atmospheric, channel, or intentional inter ferences. The received signal cannot be predicted deterministically, so that statistical methods are needed to describe the signal. In general, therefore, any received signal is analyzed as a random signal or process. The purpose of this book is to provide an elementary introduction to random signal analysis, estimation, filtering, and identification. The emphasis of the book is on the computational aspects as well as presentation of com mon analytical tools for systems involving random signals. The book covers random processes, stationary signals, spectral analysis, estimation, optimiz ation, detection, spectrum estimation, prediction, filtering, and identification. The book is addressed to practicing engineers and scientists. It can be used as a text for courses in the areas of random processes, estimation theory, and system identification by undergraduates and graduate students in engineer ing and science with some background in probability and linear algebra. Part of the book has been used by the author while teaching at State University of New York at Buffalo and California State University at Long Beach. Some of the algorithms presented in this book have been successfully applied to industrial projects.

In this updated edition the main thrust is on applied Kalman filtering. Chapters 1-3 provide a minimal background in random process theory and the response of linear systems to random inputs. The following chapter is devoted to Wiener filtering and the remainder of the text deals with various facets of Kalman filtering with emphasis on applications. Starred problems at the end of each chapter are computer exercises. The authors believe that programming the equations and analyzing the results of specific examples is the best way to obtain the insight that is essential in engineering work.

Focuses on applied Kalman filtering and its random signal analysis. Important to all control system and communication engineers, it emphasizes applications, computer software and associated sets of special computer problems to aid in tying together both theory and practice. Along with actual case studies, a diskette is included to enable readers to actually see how Kalman filtering works.

Good,No Highlights,No Markup,all pages are intact, Slight Shelfwear,may have the corners slightly dented, may have slight color changes/slightly damaged spine.

Advances in computers and personal navigation systems have greatly expanded the applications of Kalman filters. A Kalman filter uses information about noise and system dynamics to reduce uncertainty from noisy measurements. Common applications of Kalman filters include such fast-growing fields as autopilot systems, battery state of charge (SoC) estimation, brain-computer interface, dynamic positioning, inertial guidance systems, radar tracking, and satellite navigation systems. Brown and Hwang's bestselling textbook introduces the theory and applications of Kalman filters for senior undergraduates and graduate students. This revision updates both the research advances in variations on the Kalman filter algorithm and adds a wide range of new application examples. The book emphasizes the application of computational software tools such as MATLAB. The companion website includes M-files to assist students in applying MATLAB to solving end-of-chapter homework problems.