The main thrust of this text is introductory-level applied Kalman filtering. The main impediment to learning about Kalman filtering is not the mathematics - it is the background material in random process theory and linear systems analysis that usually causes the difficulty. Because of this, chapters 1 through 3 provide a minimal background in random process theory and the response of the linear systems to random inputs. Knowledge of this material is essential to the subject matter in the remaining chapters. The necessary prerequisite material on linear systems analysis can be found in most junior- or senior-level engineering texts on linear systems analysis or linear control systems. Chapter 4 is on Wiener filtering. Those who are primarily interested in Kalman filtering may skip this chapter, except for Section 4.7 on the discrete Wiener filter. This has relevance to the discrete Kalman filter. Chapters 5 through 11 deal with various facets of Kalman filtering, with emphasis on applications throughout.

New to this edition are end-of-chapter problems designed specifically to be solved with computational software such as Matlab. These special problems are marked with a computer icon. The discrete Kalman filter is a numerical procedure, so considerable insight can be gained into the filter's behavior through computational exercises.