Robotic Navigation and Mapping with Radar:
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Bibliographische Detailangaben
1. Verfasser: Adams, Martin (VerfasserIn)
Format: Elektronisch E-Book
Sprache:English
Veröffentlicht: Norwood Artech House 2012
Schlagworte:
Online-Zugang:FAW01
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Volltext
Beschreibung:4.3.2 Probabilistic Robotic Localization
Robotic Navigation and Mapping with Radar; Contents; Preface; Acknowledgments; Acronyms; Nomenclature; Chapter 1 Introduction; 1.1 Isn't Navigation and Mapping with Radar Solved?; 1.1.1 Applying Missile/Aircraft Guidance Technologies to Robotic Vehicles; 1.1.2 Placing Autonomous Navigation of Robotic Vehicles into Perspective; 1.2 Why Radar in Robotics? Motivation; 1.3 The Direction of Radar-based Robotics Research; 1.3.1 Mining Applications; 1.3.2 Intelligent Transportation System Applications; 1.3.3 Land-Based SLAM Applications; 1.3.4 Coastal Marine Applications; 1.4 Structure of the Book
ReferencesPART I: Fundamentals of Radar and Robotic Navigation; Chapter 2 A Brief Overview of Radar Fundamentals; 2.1 Introduction; 2.2 Radar Measurements; 2.3 The Radar Equation; 2.4 Radar Signal Attenuation; 2.5 Measurement Power Compression and Range Compensation; 2.5.1 Logarithmic Compression; 2.5.2 Range Compensation; 2.5.3 Logarithmic Compression and Range Compensation During Target Absence; 2.5.4 Logarithmic Compression and Range Compensation During Target Presence; 2.6 Radar-Range Measurement Techniques; 2.6.1 Time-of-Flight (TOF)-Pulsed Radar
2.6.2 Frequency Modulated Continuous Wave (FMCW) Radar2.6.2.2 Doppler Measurements; 2.6.2.3 Multiple Line-of-Sight Targets; 2.7 Sources of Uncertainty in Radar; 2.7.1 Sources of Uncertainty Common to All Radar Types; 2.8 Uncertainty Specific to TOF and FMCW Radar; 2.8.1 Uncertainty in TOF Radars; 2.8.2 Uncertainty in FMCW Radars; 2.9 Polar to Cartesian Data Transformation; 2.9.1 Nearest Neighbor Polar to Cartesian Data Conversion; 2.9.2 Weighted Polar to Cartesian Data Conversion; 2.10 Summary; 2.11 Bibliographical Remarks; 2.11.1 Extensions to the Radar Equation
2.11.2 Signal Propagation/Attenuation2.11.3 Range Measurement Methods; 2.11.4 Uncertainty in Radar; References; Chapter 3 An Introduction to Detection Theory; 3.1 Introduction; 3.2 Concepts of Detection Theory; 3.3 Different Approaches to Target Detection; 3.3.1 Non-adaptive Detection; 3.3.2 Hypothesis Free Modeling; 3.3.3 Stochastic-Based Adaptive Detection; 3.4 Detection Theory with Known Noise Statistics; 3.4.1 Constant CFARPfa with Known Noise Statistics; 3.4.2 Probability of Detection CFARPD with Known Noise Statistics
3.4.3 Probabilities of Missed Detection CFARPMD and Noise CFARPn with Known Noise Statistics3.5 Detection with Unknown Noise Statistics-Adaptive CFAR Processors; 3.5.1 Cell Averaging-CA-CFAR Processors; 3.5.2 Ordered Statistics-OS-CFAR Processors; 3.6 Summary; 3.7 Bibliographical Remarks; References; Chapter 4 Robotic Navigation and Mapping; 4.1 Introduction; 4.2 General Bayesian SLAM-The Joint Problem; 4.2.1 Vehicle State Representation; 4.2.2 Map Representation; 4.3 Solving Robot Mapping and Localization Individually; 4.3.1 Probabilistic Robotic Mapping
Focusing on autonomous robotic applications, this cutting-edge resource offers you 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. It examines grid based robotic mapping with radar based on measurement likelihood estimation. You find detailed coverage of simultaneous localization and Map Building (SLAM) - an area referred to as the "Holy Grail" of autonomous robotic research. The book derives an extended Kalman Filter
Beschreibung:1 Online-Ressource (377 pages)
ISBN:160807482X
1608074838
9781608074822
9781608074839

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