Robotic Navigation Mapping with Radar Hb

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Hardcover | 31 August 2012

At a Glance

Hardcover
378 Pages

Edition Type
Unabridged

Dimensions(cm)
25.4 x 17.78 x 2.24

Hardcover

$460.75

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Focusing on autonomous robotic applications, this cutting-edge resource offers a practical treatment of short-range radar processing for reliable object detection at the ground level. This unique book demonstrates probabilistic radar models and detection algorithms specifically for robotic land vehicles. Engineers and researchers may find detailed coverage of Simultaneous Localization and Map Building (SLAM) - an area referred to as the "Holy Grail" of autonomous robotics research.

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Non-Fiction Engineering & Technology Electronics & Communications Engineering Communications Engineering & Telecommunications Radar Technology Electronics Engineering Automatic Control Engineering

Preface	p. xiii
Acknowledgments	p. xv
Acronyms	p. xvii
Nomenclature	p. xxi
Introduction	p. 1
Isn't Navigation and Mapping with Radar Solved?	p. 1
Applying Missile/Aircraft Guidance Technologies to Robotic Vehicles	p. 2
Placing Autonomous Navigation of Robotic Vehicles into Perspective	p. 11
Why Radar in Robotics? Motivation	p. 12
The Direction of Radar-Based Robotics Research	p. 19
Mining Applications	p. 19
Intelligent Transportation System Applications	p. 21
Land-Based SLAM Applications	p. 24
Coastal Marine Applications	p. 25
Structure of the Book	p. 27
Reference	p. 28
Fundamentals of Radar and Robotic Navigation	p. 33
A Brief Overview of Radar Fundamentals	p. 35
Introduction	p. 35
Radar Measurements	p. 36
The Radar Equation	p. 38
Radar Signal Attenuation	p. 40
Measurement Power Compression and Range Compensation	p. 43
Logarithmic Compression	p. 44
Range Compensation	p. 44
Logarithmic Compression and Range Compensation During Target Absence	p. 45
Logarithmic Compression and Range Compensation During Target Presence	p. 47
Radar-Range Measurement Techniques	p. 51
Time-of-Flight (TOF) Pulsed Radar	p. 51
Frequency Modulated, Continuous Wave (FMCW) Radar	p. 53
Sources of Uncertainty in Radar	p. 58
Sources of Uncertainty Common to All Radar Types	p. 59
Uncertainty Specific to TOF and FMCW Radar	p. 68
Uncertainty in TOF Radars	p. 68
Uncertainty in FMCW Radars	p. 70
Polar to Cartesian Data Transformation	p. 72
Nearest Neighbor Polar to Cartesian Data Conversion	p. 73
Weighted Polar to Cartesian Data Conversion	p. 73
Summary	p. 76
Bibliographical Remarks	p. 76
Extensions to the Radar Equation	p. 76
Signal Propagation/Attenuation	p. 77
Range Measurement Methods	p. 78
Uncertainty in Radar	p. 78
References	p. 79
An Introduction to Detection Theory	p. 81
Introduction	p. 81
Concepts of Detection Theory	p. 82
Different Approaches to Target Detection	p. 84
Non-adaptive Detection	p. 84
Hypothesis Free Modeling	p. 85
Stochastic-Based Adaptive Detection	p. 86
Detection Theory with Known Noise Statistics	p. 87
Constant P^CFAR_fa with Known Noise Statistics	p. 87
Probability of Detection P^CFAR_n with Known Noise Statistics	p. 88
Probabilities of Missed Detection P^CFAR_MD and Noise P^CFAR_n with Known Noise Statistics	p. 89
Detection with Unknown Noise Statistics-Adaptive CFAR Processors	p. 89
Cell Averaging-CA-CFAR Processors	p. 90
Ordered Statistics-OS-CFAR Processors	p. 94
Summary	p. 100
Bibliographical Remarks	p. 101
References	p. 102
Robotic Navigation and Mapping	p. 105
Introduction	p. 105
General Bayesian SLAM-The Joint Problem	p. 107
Vehicle State Representation	p. 109
Map Representation	p. 112
Solving Robot Mapping and Localization Individually	p. 115
Probabilistic Robotic Mapping	p. 116
Probabilistic Robotic Localization	p. 116
Popular Robotic Mapping Solutions	p. 117
Grid-Based Robotic Mapping (GBRM)	p. 117
Feature-Based Robotic Mapping (FBRM)	p. 118
Relating Sensor Measurements to Robotic Mapping and SLAM	p. 120
Relating the Spatial Measurement Interpretation to the Mapping/SLAM State	p. 121
Relating the Detection Measurement Interpretation to the Mapping/SLAM State	p. 122
Popular FB-SLAM Solutions	p. 124
Bayesian FB-SLAM-Approximate Gaussian Solutions	p. 124
Feature Association	p. 126
Bayesian FB-SLAM-Approximate Particle Solutions	p. 128
A Factorized Solution to SLAM (FastSLAM)	p. 129
Multi-Hypothesis (MH) FastSLAM	p. 130
General Comments on Vector-Based FB SLAM	p. 130
FBRM and SLAM with Random Finite Sets	p. 133
Motivation: Why Random Finite Sets	p. 133
RFS Representations of State and Detected Features	p. 135
Bayesian Formulation with a Finite Set Feature Map	p. 137
The Probability Hypothesis Density (PHD) Estimator	p. 138
The PHD Filter	p. 142
SLAM and FBRM Performance Metrics	p. 145
Vehicle State Estimate Evaluation	p. 145
Map Estimate Evaluation	p. 145
Evaluation of FBRM and SLAM with the Second Order Wasserstein Metric	p. 146
Summary	p. 148
Bibliographical Remarks	p. 149
Grid-Based Robotic Mapping (GBRM)	p. 149
Gaussian Approximations to Bayes Theorem	p. 150
Non-Parametric Approximations to Bayesian FB-SLAM	p. 152
Other Approximations to Bayesian FB-SLAM	p. 152
Feature Association and Management	p. 155
Random Finite Sets (RFSs)	p. 156
SLAM and FBRM Evaluation Metrics	p. 156
References	p. 157
Radar Modeling and Scan Integration	p. 163
Predicting and Simulating FMCW Radar Measurements	p. 165
Introduction	p. 165
FMCW Radar Detection in the Presence of Noise	p. 166
Noise Distributions During Target Absence and Presence	p. 168
Received Power Noise Estimation	p. 168
Range Noise Estimation	p. 169
Predicting Radar Measurements	p. 173
A-Scope Prediction Based on Expected Target RCS and Range	p. 173
A-Scope Prediction Based on Robot Motion	p. 174
Quantitative Comparison of Predicted and Actual Measurements	p. 176
A-scope Prediction Results	p. 177
Single Bearing A-Scope Prediction	p. 177
360° Scan Multiple A-Scope Prediction, Based on Robot Motion	p. 179
Summary	p. 188
Bibliographical Remarks	p. 192
References	p. 193
Reducing Detection Errors and Noise with Multiple Radar Scans	p. 195
Introduction	p. 195
Radar Data in an Urban Environment	p. 196
Landmark Detection with Single Scan CA-CFAR	p. 198
Classical Scan Integration Methods	p. 198
Coherent and Noncoherent Integration	p. 198
Binary Integration Detection	p. 201
Integration Based on Target Presence Probability (TPP) Estimation	p. 204
False Alarm and Detection Probabilities for the TPP Estimator	p. 206
TPP Response to Noise: P^TPP_fa	p. 206
TPP Response to a Landmark and Noise: P^TPP_D	p. 209
Choice of _p, T^TTP (_p,l) and l	p. 210
Numerical Method for Determining T^TPP (_p,_l) and P^TTP_D	p. 211
A Comparison of Scan Integration Methods	p. 213
A Note on Multi-Path Reflections	p. 214
TPP Integration of Radar in an Urban Environment	p. 215
Qualitative Assessment of TPP Applied to A-Scope Information	p. 215
Quantitative Assessment of TPP Applied to Complete Scans	p. 215
A Qualitative Assessment of an Entire Parcking Lot Scene	p. 221
Recursive A-Scope Noise Reduction	p. 223
Single A-Scope Noise Subtraction	p. 225
Multiple A-Scope-Complete Scan Noise Subtraction	p. 227
Summary	p. 228
Bibliographical Remarks	p. 229
References	p. 230
Robotic Mapping with Known Vehicle Location	p. 233
Grid-Based Robotic Mapping with Detection Likelihood Filtering	p. 235
Introduction	p. 235
The Grid-Based Robotic Mapping (GBRM) Problem	p. 237
GBRM Based on Range Measurements	p. 239
GBRM with Detection Measurements	p. 241
Detection versus Range Measurement Models	p. 243
Mapping with Unknown Measurement Likelihoods	p. 245
Data Format	p. 245
GBRM Algorithm Overview	p. 246
Constant False Alarm Rate (CFAR) Detector	p. 247
Map Occupancy and Detection Likelihood Estimator	p. 247
Incorporation of the OS-CFAR Processor	p. 249
GBRM-ML Particle Filter Implementation	p. 250
Comparisons of Detection and Spatial-Based GBRM	p. 251
Dataset 1: Synthetic Data, Single Landmark	p. 251
Dataset 2: Real Experiments in the Parking Lot Environment	p. 252
Dataset 3: A Campus Environment	p. 259
Summary	p. 261
Bibliographical Remarks	p. 262
References	p. 263
Feature-Based Robotic Mapping with Random Finite Sets	p. 267
Introduction	p. 267
The Probability Hypothesis Density (PHD)-FBRM Filter	p. 268
PHD-FBRM Filter Implementation	p. 269
The FBRM New Feature Proposal Strategy	p. 271
Gaussian Management and State Estimation	p. 272
GMM-PHD-FBRM Pseudo Code	p. 274
PHD-FBRM Computational Complexity	p. 275
Analysis of the PHD-FBRM Filter	p. 275
Summary	p. 279
Bibliographical Remarks	p. 280
References	p. 281
Simultaneous Localization and Mapping	p. 283
Radar-Based SLAM with Random Finite Sets	p. 285
Introduction	p. 285
SLAM with the PHD Filter	p. 286
The Factorized RFS-SLAM Recursion	p. 286
PHD Mapping-Rao-Blackwellization	p. 287
PHD-SLAM	p. 288
Implementing the RB-PHD-SLAM Filter	p. 290
PHD Mapping-Implementation	p. 290
The Vehicle Trajectory-Implementation	p. 292
Estimating the Map	p. 292
GMM-PHD-SLAM Pseudo Code	p. 293
RB-PHD-SLAM Computational Complexity	p. 293
Radar-Based Comparisons of RFS and Vector-Based SLAM	p. 295
Summary	p. 299
Bibliographical Remarks	p. 299
References	p. 300
X-Band Radar-Based SLAM in an Off-Shore Environment	p. 301
Introduction	p. 301
The ASC and the Coastal Environment	p. 303
Marine Radar Feature Extraction	p. 305
Adaptive Coastal Feature Detection-OS-CFAR	p. 306
Image-Based Smoothing-Gaussian Filtering	p. 308
Image-Based Thresholding	p. 311
Image-Based Clustering	p. 311
Feature Labeling	p. 313
The Marine Based SLAM Algorithms	p. 314
The ASC Process Model	p. 314
RFS SLAM with the PHD Filter	p. 314
NN-EKF-SLAM Implementation	p. 317
Multi-Hyphothesis (MH) FastSLAM Implementation	p. 317
Comparisons of SLAM Concepts at Sea	p. 318
SLAM Trial 1-Comparing PHD and NN-EKF-SLAM	p. 318
SLAM Trial 2-Comparing RB-PHD-SLAM and MH-FastSLAM	p. 322
Summary	p. 324
Bibliographical Remarks	p. 326
References	p. 326
The Navtech FMCW MMW Radar Specifications	p. 329
Derivation of g(Z_k Z^k-1,X_k) for the RB-PHD-SLAM Filter	p. 331
The Empty Strategy	p. 331
The Single Feature Strategy	p. 332
NN-EKF and FastSLAM Feature Management	p. 333
Index	p. 335
Table of Contents provided by Ingram. All Rights Reserved.

Robotic Navigation Mapping with Radar Hb

At a Glance

Hardcover

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Defective items

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