Presenting the material from a graduate-level course on theory and computational algorithms of estimation in the Department of Electrical and Computer Engineering at the University of Connecticut, this text covers the design and evaluation of state estimation algorithms that operate in a stochastic environment. The algorithms are used for information extraction systems, remote sensing of moving objects, and other applications. Knowledge of linear systems and probability theory at the first-semester graduate level is assumed. Annotation c. Book News, Inc., Portland, OR (booknews.com)
Expert coverage of the design and implementation of state estimation algorithms for tracking and navigation Estimation with Applications to Tracking and Navigation treats the estimation of various quantities from inherently inaccurate remote observations. It explains state estimator design using a balanced combination of linear systems, probability, and statistics. The authors provide a review of the necessary background mathematical techniques and offer an overview of the basic concepts in estimation. They then provide detailed treatments of all the major issues in estimation with a focus on applying these techniques to real systems. Other features include: * Problems that apply theoretical material to real-world applications * In-depth coverage of the Interacting Multiple Model (IMM) estimator * Companion DynaEst(TM) software for MATLAB(TM) implementation of Kalman filters and IMM estimators * Design guidelines for tracking filters Suitable for graduate engineering students and engineers working in remote sensors and tracking, Estimation with Applications to Tracking and Navigation provides expert coverage of this important area.