Estimation with applications to tracking and navigation: [theory, algorithms, and software]
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| Main Authors: | , , |
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| Format: | Book |
| Language: | English |
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New York (u.a.)
Wiley
2001
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| Online Access: | Inhaltsverzeichnis |
| Item Description: | Hier auch später erschienene, unveränderte Nachdrucke |
| Physical Description: | XXIII, 558 S. graph. Darst. |
| ISBN: | 047141655X 9780471416555 |
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MARC
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| 100 | 1 | |a Bar-Shalom, Yaakov |e Verfasser |4 aut | |
| 245 | 1 | 0 | |a Estimation with applications to tracking and navigation |b [theory, algorithms, and software] |c Yaakov Bar-Shalom, X. Rong Li, Thiagalingam Kirubarajan |
| 264 | 1 | |a New York (u.a.) |b Wiley |c 2001 | |
| 300 | |a XXIII, 558 S. |b graph. Darst. | ||
| 336 | |b txt |2 rdacontent | ||
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| 700 | 1 | |a Kirubarajan, Thiagalingam |e Verfasser |4 aut | |
| 700 | 1 | |a Li, X. Rong |e Verfasser |4 aut | |
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| adam_text | Contents
PREFACE xvii
ACRONYMS xxi
MATHEMATICAL NOTATIONS xxii
1 INTRODUCTION 1
1.1 BACKGROUND 1
1.1.1 Estimation and Related Areas 1
1.1.2 Applications of Estimation 3
1.1.3 Preview of Estimation/Filtering 4
1.1.4 An Example of State Estimation: Vehicle Collision Avoidance 10
1.2 SCOPE OF THE TEXT 15
1.2.1 Objectives 15
1.2.2 Overview and Chapter Prerequisites 16
1.3 BRIEF REVIEW OF LINEAR ALGEBRA AND LINEAR SYSTEMS 19
1.3.1 Definitions and Notations 19
1.3.2 Some Linear Algebra Operations 20
1.3.3 Inversion and the Determinant of a Matrix 21
1.3.4 Orthogonal Projection of Vectors 23
1.3.5 The Gradient, Jacobian and Hessian 24
1.3.6 Eigenvalues, Eigenvectors, and Quadratic Forms 25
1.3.7 Continuous Time Linear Dynamic Systems — Controllability and Observability 27
1.3.8 Discrete Time Linear Dynamic Systems — Controllability and Observability 29
1.4 BRIEF REVIEW OF PROBABILITY THEORY 31
1.4.1 Events and the Axioms of Probability 31
1.4.2 Random Variables and Probability Density Function 33
1.4.3 Probability Mass Function 35
1.4.4 Mixed Random Variable and Mixed Probability PDF 36
1.4.5 Expectations and Moments of a Scalar Random Variable 37
1.4.6 Joint PDF of Two Random Variables 38
1.4.7 Independent Events and Independent Random Variables 41
1.4.8 Vector Valued Random Variables and Their Moments 41
1.4.9 Conditional Probability and PDF 44
1.4.10 The Total Probability Theorem 45
1.4.11 Bayes Formula 47
1.4.12 Conditional Expectations and Their Smoothing Property 50
1.4.13 Gaussian Random Variables 51
1.4.14 Joint and Conditional Gaussian Random Variables 52
1.4.15 Expected Value of Quadratic and Quartic Forms 54
1.4.16 Mixture Probability Density Functions 55
1.4.17 Chi Square Distributed Random Variables 57
1.4.18 Weighted Sum of Chi Square Random Variables 60
1.4.19 Random Processes 61
1.4.20 Random Walk and the Wiener Process 65
1.4.21 Markov Processes 66
1.4.22 Random Sequences, Markov Sequences and Markov Chains 69
ix
X CONTENTS
1.4.23 The Law of Large Numbers and the Central Limit Theorem 70
1.5 BRIEF REVIEW OF STATISTICS 72
1.5.1 Hypothesis Testing 72
1.5.2 Confidence Regions and Significance 74
1.5.3 Monte Carlo Runs and Comparison of Algorithms 79
1.5.4 Tables of the Chi Square and Gaussian Distributions 82
1.6 NOTES AND PROBLEMS 85
1.6.1 Bibliographical Notes 85
1.6.2 Problems 85
2 BASIC CONCEPTS IN ESTIMATION 89
2.1 INTRODUCTION 89
2.1.1 Outline 89
2.1.2 Basic Concepts Summary of Objectives 89
2.2 THE PROBLEM OF PARAMETER ESTIMATION 90
2.2.1 Definitions 90
2.2.2 Models for Estimation of a Parameter 91
2.3 MAXIMUM LIKELIHOOD AND MAXIMUM A POSTERIORI ESTIMATORS 92
2.3.1 Definitions of ML and MAP Estimators 92
2.3.2 MLE vs. MAP Estimator with Gaussian Prior 92
2.3.3 MAP Estimator with One Sided Exponential Prior 94
2.3.4 MAP Estimator with Diffuse Prior 95
2.3.5 The Sufficient Statistic and the Likelihood Equation 96
2.4 LEAST SQUARES AND MINIMUM MEAN SQUARE ERROR ESTIMATION 98
2.4.1 Definitions of LS and MMSE Estimators 98
2.4.2 Some LS Estimators 100
2.4.3 MMSE vs. MAP Estimator in Gaussian Noise 100
2.5 UNBIASED ESTIMATORS 101
2.5.1 Definition 101
2.5.2 Unbiasedness of an ML and a MAP Estimator 102
2.5.3 Bias in the ML Estimation of Two Parameters 102
2.6 THE VARIANCE AND MSE OF AN ESTIMATOR 104
2.6.1 Definitions of Estimator Variances 104
2.6.2 Comparison of Variances of an ML and a MAP Estimator 105
2.6.3 The Variances of the Sample Mean and Sample Variance 106
2.6.4 Estimation of the Probability of an Event 107
2.7 CONSISTENCY AND EFFICIENCY OF ESTIMATORS 108
2.7.1 Consistency 108
2.7.2 The Cramer Rao Lower Bound and the Fisher Information Matrix 109
2.7.3 Proof of the Cramer Rao Lower Bound 110
2.7.4 An Example of Efficient Estimator 112
2.7.5 Large Sample Properties of the ML Estimator 113
2.8 SUMMARY 114
2.8.1 Summary of Estimators 114
2.8.2 Summary of Estimator Properties 115
2.9 NOTES AND PROBLEMS 115
2.9.1 Bibliographical Notes 115
2.9.2 Problems 116
3 LINEAR ESTIMATION IN STATIC SYSTEMS 121
3.1 INTRODUCTION 121
3.1.1 Outline 121
3.1.2 Linear Estimation in Static Systems — Summary of Objectives 121
3.2 ESTIMATION OF GAUSSIAN RANDOM VECTORS 122
3.2.1 The Conditional Mean and Covariance for Gaussian Random Vectors 122
3.2.2 Estimation of Gaussian Random Vectors — Summary 123
3.3 LINEAR MINIMUM MEAN SQUARE ERROR ESTIMATION 123
3.3.1 The Principle of Orthogonality 123
CONTENTS Xi
3.3.2 Linear MMSE Estimation for Vector Random Variables 127
3.3.3 Linear MMSE Estimation — Summary 129
3.4 LEAST SQUARES ESTIMATION 129
3.4.1 The Batch LS Estimation 129
3.4.2 The Recursive LS Estimator 132
3.4.3 Examples and Incorporation of Prior Information 135
3.4.4 Nonlinear LS — An Example 137
3.4.5 LS Estimation — Summary 145
3.5 POLYNOMIAL FITTING 146
3.5.1 Fitting a First Order Polynomial to Noisy Measurements 146
3.5.2 Fitting a General Polynomial to a Set of Noisy Measurements 149
3.5.3 Mapping of the Estimates to an Arbitrary Time 152
3.5.4 Polynomial Fitting — Summary 154
3.6 GOODNESS OF FIT AND STATISTICAL SIGNIFICANCE OF PARAMETER
ESTIMATES 154
3.6.1 Hypothesis Testing Formulation of the Problem 154
3.6.2 The Fitting Error in a Least Squares Estimation Problem 156
3.6.3 A Polynomial Fitting Example 159
3.6.4 Order Selection in Polynomial Fitting — Summary 161
3.7 USE OF LS FOR A NONLINEAR PROBLEM: BEARINGS ONLY TARGET MOTION
ANALYSIS 161
3.7.1 The Problem 161
3.7.2 Observability of the Target Parameter in Passive Localization 162
3.7.3 The Likelihood Function for Target Parameter Estimation 163
3.7.4 The Fisher Information Matrix for the Target Parameter 164
3.7.5 The Goodness of Fit Test 167
3.7.6 Testing for Efficiency with Monte Carlo Runs 168
3.7.7 A Localization Example 169
3.7.8 Passive Localization — Summary 169
3.8 NOTES, PROBLEMS AND A PROJECT 172
3.8.1 Bibliographical Notes 172
3.8.2 Problems 172
3.8.3 PROJECT: An Interactive Program for Bearings Only Target Localization 176
4 LINEAR DYNAMIC SYSTEMS WITH RANDOM INPUTS 179
4.1 INTRODUCTION 179
4.1.1 Outline 179
4.1.2 Linear Stochastic Systems — Summary of Objectives 179
4.2 CONTINUOUS TIME LINEAR STOCHASTIC DYNAMIC SYSTEMS 180
4.2.1 The Continuous Time State Space Model 180
4.2.2 Solution of the Continuous Time State Equation 181
4.2.3 The State as a Markov Process 183
4.2.4 Propagation of the State s Mean and Covariance 184
4.2.5 Frequency Domain Approach 185
4.3 DISCRETE TIME LINEAR STOCHASTIC DYNAMIC SYSTEMS 187
4.3.1 The Discrete Time State Space Model 187
4.3.2 Solution of the Discrete Time State Equation 189
4.3.3 The State as a Markov Process 190
4.3.4 Propagation of the State s Mean and Covariance 191
4.3.5 Frequency Domain Approach 192
4.4 SUMMARY 195
4.4.1 Summary of State Space Representation 195
4.4.2 Summary of Prewhitening 195
4.5 NOTES AND PROBLEMS 196
4.5.1 Bibliographical Notes 196
4.5.2 Problems 196
Xii CONTENTS
5 STATE ESTIMATION IN DISCRETE TIME LINEAR DYNAMIC SYSTEMS 199
5.1 INTRODUCTION 199
5.1.1 Outline 199
5.1.2 Discrete Time Linear Estimation — Summary of Objectives 199
5.2 LINEAR ESTIMATION IN DYNAMIC SYSTEMS — THE KALMAN FILTER 200
5.2.1 The Dynamic Estimation Problem 200
5.2.2 Dynamic Estimation as a Recursive Static Estimation 202
5.2.3 Derivation of the Dynamic Estimation Algorithm 204
5.2.4 Overview of the Kalman Filter Algorithm 207
5.2.5 The Matrix Riccati Equation 211
5.2.6 Properties of the Innovations and the Likelihood Function of the System Model 213
5.2.7 The Innovations Representation 214
5.2.8 Some Orthogonality Properties 215
5.2.9 The Kalman Filter — Summary 215
5.3 EXAMPLE OF A FILTER 218
5.3.1 The Model 218
5.3.2 Results for a Kalman Filter 219
5.3.3 A Step by Step Demonstration of DynaEstâ„¢ 219
5.4 CONSISTENCY OF STATE ESTIMATORS 232
5.4.1 The Problem of Filter Consistency 232
5.4.2 Definition and the Statistical Tests for Filter Consistency 234
5.4.3 Examples of Filter Consistency Testing 237
5.4.4 Absolute Errors 243
5.4.5 Filter Consistency — Summary 244
5.5 INITIALIZATION OF STATE ESTIMATORS 245
5.5.1 Initialization and Consistency 245
5.5.2 Initialization in Simulations 246
5.5.3 A Practical Implementation in Tracking 247
5.5.4 Filter Initialization — Summary 248
5.6 SENSITIVITY 248
5.6.1 Model Mismatch 249
5.6.2 Reduced Order Filters 254
5.6.3 Suboptimal Gains 256
5.6.4 Examples of Modeling Errors and Filter Approximations 256
5.7 NOTES AND PROBLEMS 261
5.7.1 Bibliographical Notes 261
5.7.2 Problems 261
5.7.3 Computer Applications 265
6 ESTIMATION FOR KINEMATIC MODELS 267
6.1 INTRODUCTION 267
6.1.1 Outline 267
6.1.2 Kinematic Models — Summary of Objectives 267
6.2 DISCRETIZED CONTINUOUS TIME KINEMATIC MODELS 268
6.2.1 The Kinematic Models 268
6.2.2 Continuous White Noise Acceleration Model 269
6.2.3 Continuous Wiener Process Acceleration Model 270
6.3 DIRECT DISCRETE TIME KINEMATIC MODELS 272
6.3.1 Introduction 272
6.3.2 Discrete White Noise Acceleration Model 273
6.3.3 Discrete Wiener Process Acceleration Model 274
6.3.4 Kinematic Models — Summary 275
6.4 EXPLICIT FILTERS FOR NOISELESS KINEMATIC MODELS 276
6.4.1 LS Estimation for Noiseless Kinematic Models 276
6.4.2 The KF for Noiseless Kinematic Models 276
6.5 STEADY STATE FILTERS FOR NOISY KINEMATIC MODELS 277
6.5.1 The Problem 277
6.5.2 Derivation Methodology for the Alpha Beta Filter 278
CONTENTS Xiii
6.5.3, The Alpha Beta Filter for the DWNA Model 280
6.5.4 The Alpha Beta Filter for the Discretized CWNA Model 286
6.5.5 The Alpha Beta Gamma Filter for the DWPA Model 289
6.5.6 A System Design Example for Sampling Rate Selection 292
6.5.7 Alpha Beta and Alpha Beta Gamma Filters — Summary 293
6.6 NOTES AND PROBLEMS 294
6.6.1 Bibliographical Notes 294
6.6.2 Problems 295
7 COMPUTATIONAL ASPECTS OF ESTIMATION 301
7.1 INTRODUCTION 301
7.1.1 Implementation of Linear Estimation 301
7.1.2 Outline 302
7.1.3 Computational Aspects — Summary of Objectives 303
7.2 THE INFORMATION FILTER 303
7.2.1 Recursions for the Information Matrices 303
7.2.2 Overview of the Information Filter Algorithm 306
7.2.3 Recursion for the Information Filter State 307
7.3 SEQUENTIAL PROCESSING OF MEASUREMENTS 308
7.3.1 Block vs. Sequential Processing 308
7.3.2 The Sequential Processing Algorithm 309
7.4 SQUARE ROOT FILTERING 311
7.4.1 The Steps in Square Root Filtering 311
7.4.2 The LDL Factorization 312
7.4.3 The Predicted State Covariance 312
7.4.4 The Filter Gain and the Updated State Covariance 314
7.4.5 Overview of the Square Root Sequential Scalar Update Algorithm 315
7.4.6 The Gram Schmidt Orthogonalization Procedure 315
7.5 NOTES AND PROBLEMS 317
7.5.1 Bibliographical Notes 317
7.5.2 Problems 317
8 EXTENSIONS OF DISCRETE TIME LINEAR ESTIMATION 319
8.1 INTRODUCTION 319
8.1.1 Outline 319
8.1.2 Extensions of Estimation — Summary of Objectives 319
8.2 AUTOCORRELATED PROCESS NOISE 320
8.2.1 The Autocorrelated Process Noise Problem 320
8.2.2 An Exponentially Autocorrelated Noise 321
8.2.3 The Augmented State Equations 322
8.2.4 Estimation with Autocorrelated Process Noise — Summary 324
8.3 CROSS CORRELATED MEASUREMENT AND PROCESS NOISE 324
8.3.1 Cross Correlation at the Same Time 324
8.3.2 Cross Correlation One Time Step Apart 326
8.3.3 State Estimation with Decorrelated Noise Sequences — Summary 327
8.4 AUTOCORRELATED MEASUREMENT NOISE 327
8.4.1 Whitening of the Measurement Noise 327
8.4.2 The Estimation Algorithm with the Whitened Measurement Noise 329
8.4.3 Autocorrelated Measurement Noise — Summary 330
8.5 PREDICTION 330
8.5.1 Types of Prediction 330
8.5.2 The Algorithms for the Different Types of Prediction 330
8.5.3 Prediction — Summary 332
8.6 SMOOTHING 333
8.6.1 Types of Smoothing 333
8.6.2 Fixed Interval Smoothing 334
8.6.3 Overview of Smoothing 337
8.6.4 Smoothing — Summary 338
Xiv CONTENTS
8.7 NOTES AND PROBLEMS 338
8.7.1 Bibliographical Notes 338
8.7.2 Problems 338
9 CONTINUOUS TIME LINEAR STATE ESTIMATION 341
9.1 INTRODUCTION 341
9.1.1 Outline 341
9.1.2 Continuous Time Estimation — Summary of Objectives 341
9.2 THE CONTINUOUS TIME LINEAR STATE ESTIMATION FILTER 342
9.2.1 The Continuous Time Estimation Problem 342
9.2.2 Connection Between Continuous and Discrete Time Representations and Their Noise
Statistics 343
9.2.3 The Continuous Time Filter Equations 345
9.2.4 The Continuous Time Innovation 347
9.2.5 Asymptotic Properties of the Continuous Time Riccati Equation 349
9.2.6 Examples of Continuous Time Filters 351
9.2.7 Overview of the Kalman Bucy Filter 353
9.2.8 Continuous Time State Estimation — Summary 354
9.3 PREDICTION AND THE CONTINUOUS DISCRETE FILTER 355
9.3.1 Prediction of the Mean and Covariance 355
9.3.2 The Various Types of Prediction 356
9.3.3 The Continuous Discrete Filter 357
9.4 DUALITY OF ESTIMATION AND CONTROL 358
9.4.1 The Two Problems 358
9.4.2 The Solutions to the Estimation and the Control Problems 359
9.4.3 Properties of the Solutions 360
9.5 THE WIENER HOPF PROBLEM 362
9.5.1 Formulation of the Problem 362
9.5.2 The Wiener Hopf Equation 362
9.6 NOTES AND PROBLEMS 366
9.6.1 Bibliographical Notes 366
9.6.2 Problems 367
10 STATE ESTIMATION FOR NONLINEAR DYNAMIC SYSTEMS 371
10.1 INTRODUCTION 371
10.1.1 Outline 371
10.1.2 Nonlinear Estimation — Summary of Objectives 371
10.2 ESTIMATION IN NONLINEAR STOCHASTIC SYSTEMS 372
10.2.1 The Model 372
10.2.2 The Optimal Estimator 373
10.2.3 Proof of the Recursion of the Conditional Density of the State 374
10.2.4 Example of Linear vs. Nonlinear Estimation of a Parameter 376
10.2.5 Estimation in Nonlinear Systems with Additive Noise 379
10.2.6 Optimal Nonlinear Estimation — Summary 381
10.3 THE EXTENDED KALMAN FILTER 381
10.3.1 Approximation of the Nonlinear Estimation Problem 381
10.3.2 Derivation of the EKF 383
10.3.3 Overview of the EKF Algorithm 385
10.3.4 An Example: Tracking with an Angle Only Sensor 387
10.3.5 The EKF — Summary 394
10.4 ERROR COMPENSATION IN LINEARIZED FILTERS 395
10.4.1 Some Heuristic Methods 395
10.4.2 An Example of Use of the Fudge Factor 396
10.4.3 An Example of Debiasing: Conversion from Polar to Cartesian 397
10.4.4 Error Compensation in Linearized Filters — Summary 402
10.5 SOME ERROR REDUCTION METHODS 404
10.5.1 Improved State Prediction 404
10.5.2 The Iterated Extended Kalman Filter 404
CONTENTS XV
10.5.3 Some Error Reduction Methods — Summary 407
10.6 MAXIMUM A POSTERIORI TRAJECTORY ESTIMATION VIA DYNAMIC
PROGRAMMING 407
10.6.1 The Approach 407
10.6.2 The Dynamic Programming for Trajectory Estimation 408
10.7 Nonlinear Continuous Discrete Filter 409
10.7.1 The Model 409
10.7.2 The Fokker Planck Equation 410
10.7.3 Example 413
10.8 NOTES, PROBLEMS AND A PROJECT 414
10.8.1 Bibliographical Notes 414
10.8.2 Problems 414
10.8.3 Project — Nonlinear Filtering with Angle Only Measurements 419
11 ADAPTIVE ESTIMATION AND MANEUVERING TARGETS 421
11.1 INTRODUCTION 421
11.1.1 Adaptive Estimation — Outline 421
11.1.2 Adaptive Estimation — Summary of Objectives 423
11.2 ADJUSTABLE LEVEL PROCESS NOISE 424
11.2.1 Continuous Noise Level Adjustment 424
11.2.2 Process Noise with Several Discrete Levels 424
11.2.3 Adjustable Level Process Noise — Summary 426
11.3 INPUT ESTIMATION 427
11.3.1 The Model 427
11.3.2 The Innovations as a Linear Measurement of the Unknown Input 428
11.3.3 Estimation of the Unknown Input 429
11.3.4 Correction of the State Estimate 430
11.3.5 Input Estimation — Summary 431
11.4 THE VARIABLE STATE DIMENSION APPROACH 431
11.4.1 The Approach 431
11.4.2 The Maneuver Detection and Model Switching 432
11.4.3 Initialization of the Augmented Model 433
11.4.4 VSD Approach — Summary 434
11.5 A COMPARISON OF ADAPTIVE ESTIMATION METHODS FOR MANEUVERING
TARGETS 435
11.5.1 The Problem 435
11.5.2 The White Noise Model with Two Levels 436
11.5.3 The IE and VSD Methods 436
11.5.4 Statistical Test for Comparison of the IE and VSD Methods 438
11.5.5 Comparison of Several Algorithms — Summary 440
11.6 THE MULTIPLE MODEL APPROACH 441
11.6.1 Formulation of the Approach 441
11.6.2 The Static Multiple Model Estimator 441
11.6.3 The Dynamic Multiple Model Estimator 444
11.6.4 The GPB1 Multiple Model Estimator for Switching Models 447
11.6.5 The GPB2 Multiple Model Estimator for Switching Models 449
11.6.6 The Interacting Multiple Model Estimator 453
11.6.7 An Example with the IMM Estimator 457
11.6.8 Use of DynaEstâ„¢ to Design an IMM Estimator 460
11.6.9 The Multiple Model Approach — Summary 465
11.7 DESIGN OF AN IMM ESTIMATOR FOR ATC TRACKING 466
11.7.1 ATC Motion Models 466
11.7.2 The EKF for the Coordinated Turn Model 468
11.7.3 Selection of Models and Parameters 470
11.7.4 The ATC Scenario 471
11.7.5 Results and Discussion 472
11.8 WHEN IS AN IMM ESTIMATOR NEEDED? 476
11.8.1 Kalman Filter vs. IMM Estimator 477
Xvi CONTENTS
11.9 USE OF EKF FOR SIMULTANEOUS STATE AND PARAMETER ESTIMATION 481
11.9.1 Augmentation of the State 481
11.9.2 An Example of Use of the EKF for Parameter Estimation 482
11.9.3 EKF for Parameter Estimation — Summary 484
11.10 NOTES, PROBLEMS, AND TERM PROJECT 484
11.10.1 Bibliographical Notes 484
11.10.2 Problems 485
11.10.3 Term Project — IMM Estimator for Air Traffic Control 488
12 INTRODUCTION TO NAVIGATION APPLICATIONS 491
12.1 INTRODUCTION 491
12.1.1 Navigation Applications — Outline 491
12.1.2 Navigation Applications — Summary of Objectives 492
12.2 COMPLEMENTARY FILTERING FOR NAVIGATION 492
12.2.1 The Operation of Complementary Filtering 492
12.2.2 The Implementation of Complementary Filtering 493
12.3 INERTIAL NAVIGATION SYSTEMS 495
12.4 MODELS FOR INERTIAL NAVIGATION SYSTEMS 496
12.4.1 State Models 496
12.4.2 Sensor Error Models 496
12.4.3 Single Axis Models 497
12.4.4 Three Axis Models 499
12.4.5 Coordinate Transformation 500
12.5 THE GLOBAL POSITIONING SYSTEM (GPS) 501
12.5.1 The GPS Segments 502
12.5.2 GPS Satellite Constellation 502
12.6 GPS POSITIONING 502
12.6.1 The GPS Principle 502
12.6.2 The GPS Signals 503
12.6.3 The GPS Observables 506
12.7 THE ACCURACY OF GPS POSITIONING 507
12.7.1 Dilution of Precision 507
12.7.2 GPS Positioning Accuracy 509
12.8 STATE SPACE MODELS FOR GPS 511
12.8.1 Models for Receiver Clock State 511
12.8.2 Dynamic Models 512
12.8.3 Linearized Measurement Model 512
12.8.4 A Model for Exponentially Autocorrelated Noise 513
12.8.5 Coordinate Transformation 515
12.9 EXAMPLE: GPS NAVIGATION WITH IMM ESTIMATOR 515
12.9.1 Generation of Satellite Trajectories 516
12.9.2 Generation of Trajectories and Pseudorange Measurements 517
12.9.3 State Space Models 518
12.9.4 Simulation Results and Discussion 520
12.9.5 Do We Need an IMM Estimator for GPS? 523
12.10 INTEGRATED NAVIGATION 523
32.10.1 Integration by Complementary Filtering 524
12.10.2 Example 525
12.10.3 Integration by Centralized Estimation Fusion 527
12.10.4 Integration by Distributed Estimation Fusion 528
12.11 NOTES AND PROBLEMS 530
12.11.1 Bibliographical Notes 530
12.11.2 Problems 530
12.11.3 Term Project — Extended Kalman Filter for GPS 533
BIBLIOGRAPHY 537
INDEX 547
|
| any_adam_object | 1 |
| author | Bar-Shalom, Yaakov Kirubarajan, Thiagalingam Li, X. Rong |
| author_facet | Bar-Shalom, Yaakov Kirubarajan, Thiagalingam Li, X. Rong |
| author_role | aut aut aut |
| author_sort | Bar-Shalom, Yaakov |
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| building | Verbundindex |
| bvnumber | BV017183443 |
| callnumber-first | T - Technology |
| callnumber-label | TJ214 |
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| callnumber-search | TJ214.5 |
| callnumber-sort | TJ 3214.5 |
| callnumber-subject | TJ - Mechanical Engineering and Machinery |
| classification_rvk | SK 880 |
| classification_tum | MAT 496f MAT 625f VER 539f |
| ctrlnum | (OCoLC)248663963 (DE-599)BVBBV017183443 |
| dewey-full | 681.2 |
| dewey-hundreds | 600 - Technology (Applied sciences) |
| dewey-ones | 681 - Precision instruments and other devices |
| dewey-raw | 681.2 |
| dewey-search | 681.2 |
| dewey-sort | 3681.2 |
| dewey-tens | 680 - Manufacture of products for specific uses |
| discipline | Handwerk und Gewerbe / Verschiedene Technologien Mathematik Verkehrstechnik |
| format | Book |
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| id | DE-604.BV017183443 |
| illustrated | Illustrated |
| indexdate | 2024-07-09T19:14:41Z |
| institution | BVB |
| isbn | 047141655X 9780471416555 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-010354864 |
| oclc_num | 248663963 |
| open_access_boolean | |
| owner | DE-91G DE-BY-TUM DE-20 DE-29T DE-83 DE-706 DE-91 DE-BY-TUM DE-573 |
| owner_facet | DE-91G DE-BY-TUM DE-20 DE-29T DE-83 DE-706 DE-91 DE-BY-TUM DE-573 |
| physical | XXIII, 558 S. graph. Darst. |
| publishDate | 2001 |
| publishDateSearch | 2001 |
| publishDateSort | 2001 |
| publisher | Wiley |
| record_format | marc |
| spelling | Bar-Shalom, Yaakov Verfasser aut Estimation with applications to tracking and navigation [theory, algorithms, and software] Yaakov Bar-Shalom, X. Rong Li, Thiagalingam Kirubarajan New York (u.a.) Wiley 2001 XXIII, 558 S. graph. Darst. txt rdacontent n rdamedia nc rdacarrier Hier auch später erschienene, unveränderte Nachdrucke Zielverfolgung (DE-588)4190799-1 gnd rswk-swf Schätztheorie (DE-588)4121608-8 gnd rswk-swf Schätztheorie (DE-588)4121608-8 s Zielverfolgung (DE-588)4190799-1 s DE-604 Kirubarajan, Thiagalingam Verfasser aut Li, X. Rong Verfasser aut HBZ Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=010354864&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Bar-Shalom, Yaakov Kirubarajan, Thiagalingam Li, X. Rong Estimation with applications to tracking and navigation [theory, algorithms, and software] Zielverfolgung (DE-588)4190799-1 gnd Schätztheorie (DE-588)4121608-8 gnd |
| subject_GND | (DE-588)4190799-1 (DE-588)4121608-8 |
| title | Estimation with applications to tracking and navigation [theory, algorithms, and software] |
| title_auth | Estimation with applications to tracking and navigation [theory, algorithms, and software] |
| title_exact_search | Estimation with applications to tracking and navigation [theory, algorithms, and software] |
| title_full | Estimation with applications to tracking and navigation [theory, algorithms, and software] Yaakov Bar-Shalom, X. Rong Li, Thiagalingam Kirubarajan |
| title_fullStr | Estimation with applications to tracking and navigation [theory, algorithms, and software] Yaakov Bar-Shalom, X. Rong Li, Thiagalingam Kirubarajan |
| title_full_unstemmed | Estimation with applications to tracking and navigation [theory, algorithms, and software] Yaakov Bar-Shalom, X. Rong Li, Thiagalingam Kirubarajan |
| title_short | Estimation with applications to tracking and navigation |
| title_sort | estimation with applications to tracking and navigation theory algorithms and software |
| title_sub | [theory, algorithms, and software] |
| topic | Zielverfolgung (DE-588)4190799-1 gnd Schätztheorie (DE-588)4121608-8 gnd |
| topic_facet | Zielverfolgung Schätztheorie |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=010354864&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
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