Estimation with Applications to Tracking and Navigation: Theory Algorithms and Software
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.
Basic Concepts in Estimation.
Linear Estimation in Static Systems.
Linear Dynamic Systems with Random Inputs.
State Estimation in Discrete-Time Linear Dynamic Systems.
Estimation for Kinematic Models.
Computational Aspects of Estimation.
Extensions of Discrete-Time Linear Estimation.
Continuous-Time Linear State Estimation.
State Estimation for Nonlinear Dynamic Systems.
Adaptive Estimation and Maneuvering Targets.
Introduction to Navigation Applications.
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.