Automotive radars, along with other sensors such as lidar, (which stands for "light detection and ranging"), ultrasound, and cameras, form the backbone of self-driving cars and advanced driver assistant systems (ADASs). These technological advancements are enabled by extremely complex systems with a long signal processing path from radars/sensors to the controller. Automotive radar systems are responsible for the detection of objects and obstacles, their position, and speed relative to the vehicle. The development of signal processing techniques along with progress in the millimeter-wave (mm-wave) semiconductor technology plays a key role in automotive radar systems. Various signal processing techniques have been developed to provide better resolution and estimation performance in all measurement dimensions: range, azimuth-elevation angles, and velocity of the targets surrounding the vehicles. This article summarizes various aspects of automotive radar signal processing techniques, including waveform design, possible radar architectures, estimation algorithms, implementation complexity-resolution trade off, and adaptive processing for complex environments, as well as unique problems associated with automotive radars such as pedestrian detection. We believe that this review article will combine the several contributions scattered in the literature to serve as a primary starting point to new researchers and to give a bird's-eye view to the existing research community.

Automotive Radars: A review of signal processing techniques

Autonomous vehicle (AV) technology can provide a safe and convenient transportation solution for the public, but the complex and various environments in the real world make it difficult to operate safely and reliably. A connected autonomous vehicle (CAV) is an AV with vehicle connectivity capability, which enhances the situational awareness of the AV and enables the cooperation between AVs. Hence, CAV technology can enhance the capabilities and robustness of AV to be a promising transportation solution in the future. This paper introduces a representative architecture of CAVs and surveys the latest research advances, methods, and algorithms for sensing, perception, planning, and control of CAVs. It reviews the state-of-the-art and state-of-the-practice (when applicable) of a multi-layer Perception-Planning-Control architecture including on-board sensors and vehicular communications, the methods of sensor fusion and localization and mapping in the perception layer, the algorithms of decision making and trajectory planning in the planning layer, and the control strategies of trajectory tracking in the control layer. Furthermore, the implementations and impact of vehicle connectivity and the corresponding consequential challenges of cooperative perception, complex connected decision making, and multi-vehicle controls are summarized and their significant research issues enumerated. Most importantly, the critical review in this paper provides a list and discussion of the remaining challenges and unsolved problems of CAVs in each Section which would be helpful to researchers in the field. The comprehensive coverage of this paper makes it particularly useful to academic researchers, practitioners, and students alike.

Research Advances and Challenges of Autonomous and Connected Ground Vehicles

In some applications, it is necessary to analyze multi-component non-stationary signals whose components severely overlap in the time-frequency (T-F) domain. Separating those signal components is desired but very challenging for existing methods. To address this issue, we propose a novel non-parametric algorithm called ridge path regrouping (RPRG) to extract the instantaneous frequencies (IFs) of the overlapped components from a T-F representation (TFR). The RPRG first detects the ridges of a multi-component signal from a TFR and then extracts the desired IFs by regrouping the ridge curves according to their variation rates at the intersections. After the IFs are obtained, component separation is achieved by using the intrinsic chirp component decomposition (ICCD) method. Different from traditional T-F filter-based methods, the ICCD can accurately reconstruct overlapped components by using a joint-estimation scheme. Finally, applications of separating some simulated and experimental micro-Doppler signals are presented to show the effectiveness of the method.

Separation of Overlapped Non-Stationary Signals by Ridge Path Regrouping and Intrinsic Chirp Component Decomposition

Micro-Doppler signals refer to Doppler scattering returns produced by the motions of the target other than gross translation. The small micro-motions of a subject, and even just parts of a subject, can be observed through the micro-Doppler signature it creates in response to an active emitter such as a radar, laser, and even acoustic emitters. These micro-Doppler signatures are produced by the kinematic properties of the subject's motion and can be used to extract the salient features of the subject's motion, and often, identify the subject. The rapidly declining cost of micro-Doppler-capable active sensors like radar with their dramatically improving capabilities, provide significant motivation in developing micro-Doppler techniques that can improve the exploitation of these sensors. Micro-Doppler techniques aim at extracting the micro-motion of the subject that may be unique to a particular subject class or activity in order to distinguish probable false alarms from real detections, as well as to increase the value of the information extracted from the sensor. The source of micro-motion depends on the subject and can be a rotating propeller on a fixed-wing aircraft, the multiple spinning rotor blades of a helicopter, or an unmanned aerial vehicle (UAV); the vibrations of an engine shaking a vehicle; an antenna rotating on a ship; the flapping wings of birds; the swinging arms and legs of a walking person; and many other sources. Confuser detections, such as birds for UAVs or animals for humans, can be interpreted as false alarms for a sensor system, so using the available micro-Doppler returns for classification can significantly reduce the sensor false alarm rate, thereby improving the utility of the sensor system. This study reviews the current research in micro-Doppler based on subject type, sensor capabilities, as well as environmental effects, and then proposes future research areas for micro-Doppler.

https://ietresearch.onlinelibrary.wiley.com/doi/pdf/10.1049/iet-rsn.2015.0118

Review of micro-Doppler signatures

A novel feature extraction method based on micro-Doppler signature is proposed to categorize ground moving targets into three kinds, i.e., single walking person, two people walking, and a moving wheeled vehicle. Signal models and measured data from a low-resolution radar are first analyzed to find the differences between the micro-Doppler signatures from the three kinds of considered targets. Then, such discriminative micro-Doppler signatures are represented by a 3-D feature vector extracted from the time-frequency spectrograms. In the experiments based on the measured data, the ratio of the between-class distance to the within-class distance, which is defined based on Fisher discriminant analysis, is exploited to assess the discriminative ability of the 3-D feature vector. Moreover, support vector machine classifier is utilized to evaluate the classification performance. Experimental results show that the proposed micro-Doppler features can achieve a good discriminative ability and a satisfactory classification performance.

Micro-Doppler Feature Extraction Based on Time-Frequency Spectrogram for Ground Moving Targets Classification With Low-Resolution Radar

A novel method is proposed for moving wheeled vehicle and tracked vehicle classification using micro-Doppler features from returned radar signals within short dwell time. In this method, an adaptive analysis technique called Empirical Mode Decomposition (EMD) is utilized to decompose the motion components of moving vehicles, and a hierarchical classification structure using the decomposition results of returned signals is proposed to discriminate the two kinds of vehicles. The first stage of the structure elementarily identifies the tracked vehicle data by checking the existence of its unique feature and a further classification via our proposed features based on EMD is implemented in the second stage by using Support Vector Machine (SVM) classifier. Experimental results based on the simulated data and measured data are presented, including the performance analysis for low signal-to-noise ratio (SNR) case, generalization evaluation for different target circumstances and comparison with some related methods.

Hierarchical Classification of Moving Vehicles Based on Empirical Mode Decomposition of Micro-Doppler Signatures

A novel classification scheme is proposed to categorize airplane targets into three kinds, i.e., turbojet aircraft, prop aircraft, and helicopter based on the jet engine modulation (JEM) characteristics of their low resolution radar echoes. From the pattern classification viewpoint, the low-dimensional feature vector is extracted via a two-step feature extraction algorithm based on empirical mode decomposition method and CLEAN technique. The feature extraction method can separate the fuselage component and JEM component in the target echo, and sufficiently utilize the information within or between the two components to extract the discriminative features for the three kinds of aircrafts. In addition, because the noise level of a test sample is usually different from those of the training samples in the real application, a simple and efficient preprocessing method is proposed for the classification stage to denoise the received test sample. Experimental results based on the simulated and measured data are presented, including the performance analysis for different dwell time, pulse repeat frequency (PRF) or signal-noise-ratio (SNR) cases and comparison with some related methods.

Robust Classification Scheme for Airplane Targets With Low Resolution Radar Based on EMD-CLEAN Feature Extraction Method

Micro-Doppler information contained in the returned echo reflects the special structure of moving vehicles and can be utilized to discriminate wheeled vehicle and tracked vehicle. In this paper, generalized matched filter is employed to suppress clutter and a preprocessing is used to eliminate the influence of bulk velocity for Doppler spectrum firstly. Then features which depict the distinction between the wheeled and tracked vehicles are extracted and experimental results based on extracted features are presented.

Moving vehicle classification based on micro-Doppler signature

The invention discloses a ground moving target classification method based on general matched filtering, which is mainly used for solving the problem of nonlinear modulation of the existing moving target display technology to a target Doppler spectrum at the clutter suppression stage. The implementation process is as follows: estimating a clutter autocorrelation matrix; calculating a whitening matrix of the clutter autocorrelation matrix; utilizing the whitening matrix to carry out whitening treatment on clutter components in a time-domain echo signal containing a target, so as to obtain the time-domain echo signal after whitening clutter; carrying out matching treatment on the time-domain echo signal after whitening the clutter so as to obtain the Doppler spectrum with the clutter components removed; and carrying out feature extraction on the Doppler spectrum with the clutter components removed, and using the identification algorithm to identify extracted feature. By using the method, the nonlinear modulation role of the moving target display technology to the target Doppler spectrum can be eliminated, the impacts of the clutter components on the identification result can be removed, and the ground moving targets with rotating parts can be identified.

Ground moving target classification method based on general matched filtering

The invention discloses a method for steadily classifying a ground moving target based on super-resolution Doppler spectrum, and mainly solves the problem that the classification performance is low because the signal structure is influenced during clutter reduction, the resolution ratio is low under short residence time and the noise cannot be suppressed in the conventional similar method. The method comprises the following steps of: calculating a short-time echo signal Doppler spectrum, and estimating the noise energy in the signal by utilizing the short-time echo signal Doppler spectrum; estimating a clutter autocorrelation matrix by utilizing a target approach distance unit; establishing a Fourier-based dictionary matrix, and solving the l1 norm optimization problem to obtain a super-resolution Doppler spectrum of a target; extracting characteristics of the super-resolution Doppler spectrum of the target; and classifying the extracted characteristics by using a classifier. By the method, the resolution ratio of the Doppler spectrum of the target is improved, the signal structure can be kept during adaptive clutter reduction, and the noise in the signal is suppressed; and moreover, the classification performance is improved, the noise robustness is obtained, and the method can be used for classifying moving vehicle targets with maneuvering parts.

Yanbing Li

Papers

Hierarchical Classification of Moving Vehicles Based on Empirical Mode Decomposition of Micro-Doppler Signatures

Robust Classification Scheme for Airplane Targets With Low Resolution Radar Based on EMD-CLEAN Feature Extraction Method

Moving vehicle classification based on micro-Doppler signature

Ground moving target classification method based on general matched filtering

Method for steadily classifying ground moving target based on super-resolution Doppler spectrum