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

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

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 c. Book News, Inc., Portland, OR (
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Product Details

  • ISBN-13: 9780890066430
  • Publisher: Artech House, Incorporated
  • Publication date: 1/28/1993
  • Edition description: BK&DISK
  • Edition number: 1
  • Pages: 511
  • Product dimensions: 8.88 (w) x 11.36 (h) x 1.42 (d)

Table of Contents

Mathematical Notations
1 Introduction 1
1.2 Scope of the Text 5
1.3 Brief Review of Linear Algebra and Linear Systems 9
1.4 Brief Review of Probability Theory 23
1.5 Brief Review of Statistics 66
2 Basic Concepts in Estimation 85
2.2 The Problem of Parameter Estimation 87
2.3 Maximum Likelihood and Maximum a Posteriori Estimators 89
2.4 Least Squares and Minimum Mean Square Error Estimation 97
2.5 Unbiased Estimators 101
2.6 The Variance of an Estimator 105
2.7 Consistency and Efficiency of Estimators 110
3 Linear Estimation in Static Systems 123
3.2 Estimation of Gaussian Random Vectors 125
3.3 Linear Minimum Mean Square Error Estimation 127
3.4 Least Squares Estimation 135
3.5 Polynomial Fitting 145
3.6 Goodness of Fit and Statistical Significance of Parameter Estimates 155
3.7 Use of LS for a Nonlinear Problem: Bearings Only Target Motion Analysis 164
4 Linear Dynamic Systems with Random Inputs 181
4.2 Continuous Time Linear Stochastic Dynamic Systems 183
4.3 Discrete Time Linear Stochastic Dynamic Systems 192
5 State Estimation in Discrete Time Linear Dynamic Systems 207
5.2 Linear Estimation in Dynamic Systems - The Kalman Filter 209
5.3 Example of a Filter 230
5.4 Consistency of State Estimators 235
5.5 Initialization of State Estimators 251
6 Estimation for Kinematic Models 259
6.2 Discretized Continuous Time Kinematic Models 261
6.3 Direct Discrete Time Kinematic Models 266
6.4 Explicit Filters for Noiseless Kinematic Models 270
6.5 Steady-State Filters for Noisy Kinematic Models 272
7 Computational Aspects of Estimation 293
7.2 The Information Filter 297
7.3 Sequential Processing of Measurements 301
7.4 Square-Root Filtering 304
8 Extensions of Discrete Time Linear Estimation 315
8.2 Autocorrelated Process Noise 317
8.3 Cross-Correlated Measurement and Process Noise 323
8.4 Autocorrelated Measurement Noise 325
8.5 Prediction 328
8.6 Smoothing 331
9 Continuous Time Linear State Estimation 339
9.2 The Continuous Time Linear State Estimation Filter 341
9.3 Prediction and the Continuous-Discrete Filter 357
9.4 Duality of Estimation and Control 361
9.5 The Wiener-Hopf Problem 366
10 State Estimation for Nonlinear Dynamic Systems 371
10.2 Estimation in Nonlinear Stochastic Systems 373
10.3 The Extended Kalman Filter 382
10.4 Error Compensation in Linearized Filters 400
10.5 Some Error Reduction Methods 406
10.6 Maximum a Posteriori Trajectory Estimation Via Dynamic Programming 411
11 Adaptive Estimation and Maneuvering Targets 417
11.2 Adjustable Level Process Noise 421
11.3 Input Estimation 426
11.4 The Variable State Dimension Approach 432
11.5 A Comparison of Several Adaptive Estimation Methods for Maneuvering Targets 437
11.6 The Multiple Model Approach 446
11.7 Use of EKF for Simultaneous State and Parameter Estimation 484
Bibliography 493
Index 501
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