Estimation and Tracking: Principles, Techniques, and Software / Edition 1

Estimation and Tracking: Principles, Techniques, and Software / Edition 1

by Yaakov Bar-Shalom, Xiao-Rong Li

This book provides the engineering tools you need for the design and evaluation of state estimators in stochastic systems. See more details below


This book provides the engineering tools you need for the design and evaluation of state estimators in stochastic systems.

Product Details

Artech House, Incorporated
Publication date:
Edition description:
Product dimensions:
8.88(w) x 11.36(h) x 1.42(d)

Related Subjects

Table of Contents

Mathematical Notations
1.2Scope of the Text5
1.3Brief Review of Linear Algebra and Linear Systems9
1.4Brief Review of Probability Theory23
1.5Brief Review of Statistics66
2Basic Concepts in Estimation85
2.2The Problem of Parameter Estimation87
2.3Maximum Likelihood and Maximum a Posteriori Estimators89
2.4Least Squares and Minimum Mean Square Error Estimation97
2.5Unbiased Estimators101
2.6The Variance of an Estimator105
2.7Consistency and Efficiency of Estimators110
3Linear Estimation in Static Systems123
3.2Estimation of Gaussian Random Vectors125
3.3Linear Minimum Mean Square Error Estimation127
3.4Least Squares Estimation135
3.5Polynomial Fitting145
3.6Goodness of Fit and Statistical Significance of Parameter Estimates155
3.7Use of LS for a Nonlinear Problem: Bearings Only Target Motion Analysis164
4Linear Dynamic Systems with Random Inputs181
4.2Continuous Time Linear Stochastic Dynamic Systems183
4.3Discrete Time Linear Stochastic Dynamic Systems192
5State Estimation in Discrete Time Linear Dynamic Systems207
5.2Linear Estimation in Dynamic Systems - The Kalman Filter209
5.3Example of a Filter230
5.4Consistency of State Estimators235
5.5Initialization of State Estimators251
6Estimation for Kinematic Models259
6.2Discretized Continuous Time Kinematic Models261
6.3Direct Discrete Time Kinematic Models266
6.4Explicit Filters for Noiseless Kinematic Models270
6.5Steady-State Filters for Noisy Kinematic Models272
7Computational Aspects of Estimation293
7.2The Information Filter297
7.3Sequential Processing of Measurements301
7.4Square-Root Filtering304
8Extensions of Discrete Time Linear Estimation315
8.2Autocorrelated Process Noise317
8.3Cross-Correlated Measurement and Process Noise323
8.4Autocorrelated Measurement Noise325
9Continuous Time Linear State Estimation339
9.2The Continuous Time Linear State Estimation Filter341
9.3Prediction and the Continuous-Discrete Filter357
9.4Duality of Estimation and Control361
9.5The Wiener-Hopf Problem366
10State Estimation for Nonlinear Dynamic Systems371
10.2Estimation in Nonlinear Stochastic Systems373
10.3The Extended Kalman Filter382
10.4Error Compensation in Linearized Filters400
10.5Some Error Reduction Methods406
10.6Maximum a Posteriori Trajectory Estimation Via Dynamic Programming411
11Adaptive Estimation and Maneuvering Targets417
11.2Adjustable Level Process Noise421
11.3Input Estimation426
11.4The Variable State Dimension Approach432
11.5A Comparison of Several Adaptive Estimation Methods for Maneuvering Targets437
11.6The Multiple Model Approach446
11.7Use of EKF for Simultaneous State and Parameter Estimation484

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