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.

The Fourth Edition to the Introduction of Random Signals and Applied Kalman Filtering is updated to cover innovations in the Kalman filter algorithm and the proliferation of Kalman filtering applications from the past decade. The text updates both the research advances in variations on the Kalman filter algorithm and adds a wide range of new application examples. Several chapters include a significant amount of new material on applications such as simultaneous localization and mapping for autonomous vehicles, inertial navigation systems and global satellite navigation systems.

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.

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.

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.