Radar is a key technology in the safety system of a modern vehicle. Automotive radars are the critical sensors in advanced driver-assistance systems, which are used in adaptive cruise control, collision avoidance, blind spot detection, lane change assistance, and parking assistance.
The book covers all the modern radars used in automotive technology. A long-range radar mounted in the front of the vehicle is usually for adaptive cruise control. The medium range radars mounted in the front and rear provide wider coverage than the long-range radars and they can be used for cross traffic alert and lane change assistance. The corner mounted short range radars support parking aid, obstacle/pedestrian detection and blind spot monitoring. In real applications, these radars usually work together to provide more robust detection results. In this book, we also recognize that the future of automotive radars should not only address conventional exterior applications, but also play important roles for interior applications, such as gesture sensing for human-vehicle interaction and driver/passenger vital signs and presence monitoring.
The book is aimed at those radar engineers who are working on automotive applications.
Author(s): Zhengyu Peng, Changzhi Li, Faruk Uysal
Series: IET Radar, Sonar and Navigation Series
Publisher: The Institution of Engineering and Technology
Year: 2022
Language: English
Pages: 324
City: London
Contents
About the Editors
1 Introduction
References
2 Principles of automotive radar systems
2.1 Basic radar functions
2.2 Automotive radar architecture
2.2.1 Transmitter
2.2.2 Receiver
2.2.3 Antenna and antenna array
2.3 Signal models
2.3.1 Amplitude models
2.3.2 Noise model
2.4 Radar waveforms and signal processing
2.4.1 Range processing
2.4.2 Doppler processing
2.4.3 Typical waveform parameters for FMCW automotive radar applications
2.4.4 Window taper function
2.5 Detection fundamentals
2.5.1 Coherent and noncoherent integration
2.5.2 Minimal SNR for certain PD and PFA
2.5.3 Constant false alarm rate detection
2.6 Radar design considerations
2.6.1 Sensitivity
2.6.2 Range/Doppler coverage
2.6.3 Range/Doppler resolution
2.6.4 Phase noise
2.6.5 Chirp non-linearity
References
3 MIMO radar technology
3.1 Virtual array synthesis via MIMO radar
3.2 Waveform orthogonality strategies in automotive MIMO radar
3.2.1 Waveform orthogonality via TDM
3.2.2 Waveform orthogonality via DDM
3.2.3 Waveform orthogonality via FDM
3.3 Angle finding in automotive MIMO radar
3.3.1 High-resolution angle finding with ULA
3.3.2 High-resolution angle finding with SLA
3.4 High-resolution imaging radar for autonomous driving
3.4.1 Cascade of multiple radar transceivers
3.4.2 Examples of cascaded imaging radars
3.4.3 Design challenges of imaging radar
3.5 Challenges in automotive MIMO radar
3.5.1 Angle finding in the presence of multipath reflections
3.5.2 Waveform orthogonality in automotive MIMO radar
3.5.3 Efficient, high-resolution angle-finding algorithms are needed
References
4 Interference and interference mitigation
4.1 Automotive radar interference
4.1.1 Signal model for radar and the interference
4.1.2 Characteristics of the automotive radar interference
4.1.3 Final remarks
4.2 Interference mitigation
4.2.1 Detection of interference
4.2.2 Interference mitigation and avoidance
4.2.3 Final remarks
Acknowledgements
References
5 mmWave radar tracking and sensor fusion with camera
5.1 Introduction
5.2 Related work
5.3 Radar-EKF: radar tracking methodology
5.3.1 FMCW radar
5.3.2 FMCW radar noise
5.3.3 EKF prediction
5.3.4 EKF update
5.3.5 Nonlinearity
5.3.6 Radar-EKF workflow
5.4 Sensor fusion with camera
5.4.1 Coordinates and EBs
5.4.2 Camera preprocessing
5.4.3 HEM
5.4.4 Fusion-EKF
5.4.5 Data association and sensor synchronization
5.4.6 EB evaluation
5.5 Sensor tracking and fusion experimental results
References
6 Automotive radar target classification
6.1 Introduction
6.2 Machine learning approaches for classification
6.2.1 Basic concept of machine learning
6.2.2 Multilayer perceptron
6.2.3 Convolutional neural network
6.2.4 Recurrent neural network
6.3 Radar target classification with micro-Doppler signature
6.3.1 Description of micro-Doppler signature
6.3.2 Classification example
6.4 Radar target classification with statistical RCS information
6.4.1 Physical meaning of RCS
6.4.2 RCS simulation in MMW band
6.4.3 Statistical representation of RCS
6.4.4 Classification with ANN
6.5 Radar target classification using radar images
6.5.1 Radar images simulation
6.5.2 Classification of radar images with CNN
6.6 Conclusion
References
7 Road condition recognition with radar
7.1 Introduction
7.2 Ground truth of road surfaces and radar metrics
7.2.1 Statistics of rough road surfaces
7.2.2 Effective permittivity of asphalt and concrete roads
7.2.3 Effective permittivity of water, ice, and snow
7.2.4 Scattering coefficient
7.3 Surface scattering model based on full-wave simulation
7.3.1 Full-wave simulation for the random rough surfaces
7.3.2 Convergence analysis and statistics of the simulated results
7.3.3 Reduced backscattering coefficients models of rough surface in mmWave band
7.3.4 Experiment results
7.4 Semi-empirical volumetric scattering model based on radiative transfer model
7.4.1 Theoretical analysis of radiative transfer method
7.4.2 Radiative transfer model with radar measurement data
7.5 Radar measurements for different road conditions at 77 GHz
7.6 Conclusion
References
8 Radar-based gesture sensing
8.1 Introduction
8.2 Fundamentals of short-range radar
8.3 Radar systems for gesture sensing
8.3.1 CW radar
8.4 FMCW radar
8.4.1 Basic theory
8.4.2 Advancements in FMCW radar
8.5 Radar in the IoT era
8.6 Conclusion
References
9 In-cabin vital sign monitoring
9.1 Introduction
9.1.1 Driving safety: emerging threats
9.1.2 Prevention methods and their contradictions
9.2 In-vehicle radar-based vital sign monitoring
9.2.1 Continuous-wave radar
9.2.2 Frequency-modulated continuous-wave radar
9.2.3 IR-UWB radar
9.3 Potential technology: self-injection-locked radar
9.3.1 Sensing principle and clutter immunity
9.3.2 EM interference and nonlinear distortion
9.3.3 Random body motion cancellation
9.3.4 Wearable SIL radar
9.3.5 Multiple subject detection using SIL radar
References
10 Conclusion
References
Index
