Estimation with Applications to Tracking and Navigation: Theory Algorithms and SoftwareExpert 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. |
Contents
| 1 | |
2 BASIC CONCEPTS IN ESTIMATION | 89 |
3 LINEAR ESTIMATION IN STATIC SYSTEMS | 121 |
4 LINEAR DYNAMIC SYSTEMS WITH RANDOM INPUTS | 179 |
5 STATE ESTIMATION IN DISCRETETIME LINEAR DYNAMIC SYSTEMS | 199 |
6 ESTIMATION FOR KINEMATIC MODELS | 267 |
7 COMPUTATIONAL ASPECTS OF ESTIMATION | 301 |
8 EXTENSIONS OF DISCRETETIME LINEAR ESTIMATION | 319 |
9 CONTINUOUSTIME LINEAR STATE ESTIMATION | 341 |
10 STATE ESTIMATION FOR NONLINEAR DYNAMIC SYSTEMS | 371 |
11 ADAPTIVE ESTIMATION AND MANEUVERING TARGETS | 421 |
12 INTRODUCTION TO NAVIGATION APPLICATIONS | 491 |
| 537 | |
| 547 | |
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Common terms and phrases
acceleration algorithm assumed autocorrelation chi-square distributed components computed constant velocity continuous-time coordinate corresponding covariance matrix CRLB defined denoted density dimension discrete discrete-time discussed DynaEst dynamic system estimation error evaluation extended Kalman filter Figure filter gain Gaussian random given IMM estimator independent innovation input interval Kalman filter kinematic models likelihood function LMMSE maneuvering index MAP estimate Markov Markov process measurement noise MMSE MMSE estimator mode Monte Carlo runs motion navigation noise sequences nonlinear normalized Note observations obtained optimal orthogonal P(kk platform polynomial prediction covariance probability region problem process noise pseudorange random variable recursion Riccati equation RMS error sampling period satellite scalar Section sensor Simulation spectral standard deviation statistic steady-state stochastic Subsection t₁ target technique time-invariant total probability theorem trajectory uncorrelated update vector white noise Wiener filter Wiener process yields zero zero-mean white σ²
Popular passages
Page 543 - Estimating Optimal Tracking Filter Performance for Manned Maneuvering Targets," IEEE Trans, on Aerospace and Electronic Systems, Vol.
Page 539 - Sequential Monte Carlo Methods in Practice. Springer- Verlag, New York, 2001.
Page xvii - Y. Bar-Shalom and XR Li, Estimation and Tracking: Principles, Techniques and Software, Artech House 1993.