Kalman Filtering with Real-Time Applications presents a thorough
discussion of the mathematical theory and computational schemes of
Kalman filtering. The filtering algorithms are derived via different
approaches, including a direct method consisting of a series of
elementary steps, and an indirect method based on innovation projection.
Other topics include Kalman filtering for systems with correlated noise
or colored noise, limiting Kalman filtering for time-invariant systems,
extended Kalman filtering for nonlinear systems, interval Kalman
filtering for uncertain systems, and wavelet Kalman filtering for
multiresolution analysis of random signals. The last two topics are new
additions to this third edition. Most filtering algorithms are
illustrated by using simplified radar tracking examples. The style of
the book is informal, and the mathematics is elementary but rigorous.
The text is self-contained, suitable for self-study, and accessible to
all readers with a minimum knowledge.