Estimation with Applications to Tracking and Navigation

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9780471416555: Estimation with Applications to Tracking and Navigation

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.

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About the Author:

YAAKOV BAR-SHALOM, PhD, is Distinguished Professor in the Department of Electrical and Computer Engineering and Director of the Estimation and Signal Processing Lab at the University of Connecticut in Storrs.
X. RONG LI, PhD, is Associate Professor in the Department of Electrical Engineering at the University of New Orleans.
THIAGALINGAM KIRUBARAJAN, PhD, is Assistant Research Professor in the Department of Electrical and Computer Engineering and Associate Director of the Estimation and Signal Processing Lab at the University of Connecticut in Storrs.

From Book News:

This text--set of lecture notes--presents the material from a second semester graduate level course on estimation offered in the Dept. of Electrical and System Engineering at the U. of Connecticut. The main goal of the course is to convey the knowledge necessary for the evaluation and design of state estimators that operate in a stochastic environment. The material covers the topics usually taught in control-oriented EE/systems and aeronautical engineering programs. The prerequisites are a solid knowledge of linear systems and probability theory at the first semester graduate level. Annotation copyright Book News, Inc. Portland, Or.

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