Automotive Radar Systems Research Articles

Bi‐static delay‐Doppler reference for cooperative passive vehicle‐to‐X radar applications

Automotive radar systems are indispensable for advanced driver assistance systems and traffic safety. Besides existing monostatic radar techniques, bi-static radar sensing like passive coherent location offers additional options to improve the radar visibility of vulnerable road users. Regarding the testing and evaluation of signal processing algorithms including parameter estimation, it is essential to provide electromagnetically shielded and reproducible measurement conditions, in addition to field tests in real traffic scenarios. This study describes the possibility to emulate relevant performance parameters for bi-static radar scenarios in the frequency range from 1 GHz to 6 GHz in a metal-shielded semi-anechoic chamber. Of special interest are the bi-static angle between transmitter, target, and receiver, and the resulting bi-static Doppler frequencies of a realistic vehicular traffic scenario. According to the concept of cooperative passive coherent location, Doppler scattering measurements are presented and compared to electromagnetic simulations. The authors find promising agreement between measured and ground truth data in the delay-Doppler spectrum.

Omnidirectional retroreflective surface using geodesic polyhedra

In this paper, we present the design and analysis of omnidirectional retroreflective surfaces made of trihedral, corner reflectors to enhance the monostatic radar cross section of otherwise low back-scattering surfaces. The proposed designs can be mounted on helmets and used for various applications such as improving the collision avoidance of automotive radar systems in the presence of bicyclists and motorcyclists or improving collision prediction radars in contact sports or other risky environments, where helmets are used. To address these applications, we analyze the coherent response of the proposed designs.

Mutual Interference Suppression Using Wavelet Denoising in Automotive FMCW Radar Systems

This paper presents an efficient mutual interference suppression method using wavelet denoising in automotive radar systems. When a radar signal transmitted from another radar-equipped vehicle flows into our radar system, it acts as an interference signal and degrades the target detection performance. With wavelet denoising, the interference signal can be extracted from the time-domain low-pass filter output. Then, the effect of the interference can be mitigated by subtracting the interference signal from the original low-pass filter output. As a result, the beat frequency containing the target information can be estimated accurately. Simulation results show that the proposed method can enhance the estimation accuracy of the target's distance, velocity, and angle. In addition, the performance of the proposed method is verified through actual experiments using heterogeneous radars. In the measurement results, even though the exact specifications of the radar signal transmitted from the other vehicle are not identified, the interference is effectively suppressed. Unlike other existing methods, the proposed method using wavelet denoising does not need to generate a specific radar waveform and it can mitigate interference only by signal processing without changing the existing hardware.

Calibration of Motional Frequency Spread for Wide-Band FMCW Automotive Millimeter-Wave Radar

As a key module for self-driving cars and advanced driver assistant systems (ADAS), frequency modulation continuous wave (FMCW) automotive millimeter-wave (mmwave) radar has a good market prospect. Due to the relative motion of target and radar in the detection window, the motional frequency spread is inevitable especially for wide-band FMCW automotive mmwave radar systems with large bandwidth and possible high-speed motion. In this paper, for wide-band FMCW automotive mmwave radar systems, we first derive analytical expressions of the motional frequency interference term and phase interference term in the intermediate frequency (IF) signal. Then, the effect of frequency spread is comprehensively analyzed with the help of numerical results. Motivated by the mechanism and influence of frequency spread, we propose three schemes for motional frequency spread calibration with different performance-complexity tradeoff. Finally, simulations verify that the proposed schemes can effectively compensate the degradation of system resolution and parameter estimation accuracy due to frequency spread.

Road Environment Recognition for Automotive FMCW RADAR Systems Through Convolutional Neural Network

In this study, we propose a method to recognize road environments with automotive frequency-modulated continuous wave (FMCW) radar systems. For automotive radar systems on the road, diverse road environments are observed. Each road environment generates unnecessary echoes known as clutter, and the magnitude distribution of received radar signal varies depending on road structures. Therefore, it is necessary to classify the road environment and adopt a target detection algorithm suitable for each road environment characteristic. To recognize the road environment in advance, it is necessary to identify the section where the road environment changes. In this paper, we define a changed road area as a transition region, and we classify the road environment and transition regions to improve the road environment recognition performance. Road environments are recognized by applying convolutional neural networks to the frequency-domain received signals of 77 GHz FMCW automotive radar systems. Experimental results in real-road environments demonstrate that the proposed method achieves 100% recognition performance, which is better if compared with that of the conventional methods.

Classification of Interference Signal for Automotive Radar Systems With Convolutional Neural Network

When a radar signal generated by another vehicle arrives at an ego-vehicle, mutual interference occurs, which can seriously degrade the detection performance of the radar. To mitigate mutual interference, the type of radar modulation used in the interference vehicle must be identified because the types of radar systems installed in each vehicle are different. Therefore, in this paper, we propose a method for classifying the modulation types of interference signals in automotive fast chirp frequency modulated continuous waveform (FMCW) radar systems. We build a mathematical model of the received signal when the radar signal transmitted by the ego-vehicle interferes with various types of interference signals, such as unmodulated continuous wave (CW), slow chirp FMCW, fast chirp FMCW, pulsed CW, and frequency-shift keying signals. In the fast chirp FMCW radar systems, the received signal is converted into range-Doppler response using two-dimensional Fourier transform. Based on range-Doppler responses of the interference signals, we design a classifier to identify the modulation type of interference signals using a convolutional neural network (CNN). Through our proposed CNN, we can classify five different types of interference signals with an accuracy of over 96%. In addition, compared to conventional feature-based machine learning techniques such as support vector machines, the proposed method can effectively identify the interference signal with fewer input signals in shorter time.

Physical-Layer Cyberattack and Interference Resilient Automotive Radars

In this paper, we present a physical-layer attack and interference resilient automotive radar system, and derive analytical upper bounds for the probability of not detecting an attack, and the probability of false attack alarm. We consider a quite general attack model and prove that if the attack signal level is above a defined relative threshold, both the probability of false attack alarm and the probability of not detecting an attack converge to zero exponentially with the number of samples acquired during a single chirp, and the number of chirps used in a frame. We also derive an analytical formula for this relative threshold, and prove that by selecting shorter frame durations, and using lower noise RF equipment, the threshold can be made as small as possible. Basically, by proper selection of radar parameters arbitrarily small attack signals can be detected almost always with almost no false alarms. We also present a numerical example using real measured data obtained from two 77 GHz automotive radars operated at the same time. Also using real data, we show that the proposed system reduces the negative effects of undetected weak attacks which are below the above mentioned threshold.

A High-Resolution Framework for Range-Doppler Frequency Estimation in Automotive Radar Systems

In this paper, a signal processing framework using high-resolution algorithms is applied to automotive radar systems for target separation purposes. In many critical use cases, targets sharing similar parameters, i.e., range and relative velocity, can hardly be discriminated in a 2-dimensional (2-D) range-Doppler spectrum via conventional radar signal processing techniques. The proposed framework by taking advantage of a 2-D model-based algorithm with high-resolution capability, i.e., modified matrix enhancement and matrix pencil (MMEMP), is able to separate the overlapping targets by jointly estimating their aforementioned target parameters in a precise manner. Additionally, to reduce computational cost, the presented framework could preselect the peaks (local maxima) of interest via a 2-D model order selection algorithm, i.e., 2-D subspace-based automatic model order selection (2-D SAMOS), and apply the MMEMP only in multi-target situations. Finally, simulation and experiments are carried out to evaluate the performance of the proposed high-resolution target separation framework.

Focus on software, data acquisition, and instrumentation

Focus on software, data acquisition, and instrumentation

Moving target detection on an iron‐bridge using phase analysis for automotive FMCW radar

When driving on an iron-bridge, stationary iron-clutters cause disturbances in the detection of a moving target in an automotive radar system. In this Letter, the authors propose a moving target detection method that utilises phase similarity of the spatial domain on an iron-bridge. To analyse the spatial domain via the array antenna signal of the up- and down-chirp beat-frequencies, the frequency-modulated continuous wave (FMCW)-phased array radar signal was used. It is possible to match the up- and down-chirp beat-frequencies of each object, moving target, and stationary iron-clutters, by using the proposed method. Not only the moving target but also the ego-vehicle and moving target velocity are detected through this matching. The suitability of the proposed method is verified by on-road measurement data using a 77 GHz FMCW-phased array radar.

Interference in Automotive Radar Systems: Characteristics, Mitigation Techniques, and Current and Future Research

This article examines the problem of interference in automotive radar. Different types of automotive radar as well as mechanisms and characteristics of interference and the effects of interference on radar system performance are described. The interference-to-noise ratio (INR) at the output of a detector is a measure of the susceptibility of a radar to interference. The INR is derived from different types of interfering and victim radars and depends on the location of both as well as parameters such as transmit power, antenna gain, and bandwidth. In addition, for victim radar with beamscanning, INR depends on the location of the target the victim radar is attempting to detect. Analysis is presented to show the effects of various interference scenarios on the INR. A review of the current state of the art in interference mitigation techniques previously deployed as well as areas of research currently being addressed is then provided. Finally, important future research directions are suggested.

On the Safe Road Toward Autonomous Driving: Phase Noise Monitoring in Radar Sensors for Functional Safety Compliance

The first approaches to improve vehicle safety were so-called passive safety systems, which did not directly interfere with the driving process but protected the occupants during a crash. In contrast, the first assistance system was the antilock braking system (ABS) successfully introduced in the early 1970s. This active system was developed to avoid an accident by automatically intervening in the braking behavior of the car. At about the same time, the first automotive radar prototype was presented. Since the invention of this very unwieldy radar system, organizations all around the world spent significant efforts in pushing the development of automotive radar systems forward. Today, radar sensors together with ultrasound sensors, lidar, and cameras form the backbone of advanced driver assistant systems (ADASs) as well as autonomous driving (AD), which is in the prototype stage. In particular, because of their robustness against adverse lighting and weather conditions, radar sensors are considered a key technology for modern vehicle safety and comfort systems. Along with the trend toward higher automation, more cars will be equipped with radar sensors in the near future. Because ADASs directly influence the vehicle dynamics, new regulating functional safety (FuSa) requirements, such as the ISO 26262 standard, were introduced. These requirements are mandatory to protect the road users.

High-Performance Automotive Radar: A Review of Signal Processing Algorithms and Modulation Schemes

The ongoing automation of driving functions in cars results in the evolution of advanced driver assistance systems (ADAS) into ones capable of highly automated driving, which will in turn progress into fully autonomous, self-driving cars. To work properly, these functions first must be able to perceive the car's surroundings by such means as radar, lidar, camera, and ultrasound sensors. As the complexity of such systems increases along with the level of automation, the demands on environment sensors, including radar, grow as well. For radar performance to meet the requirements of self-driving cars, straightforward scaling of the radar parameters is not sufficient. To refine radar capabilities to meet more stringent requirements, fundamentally different approaches may be required, including the use of more sophisticated signal processing algorithms as well as alternative radar waveforms and modulation schemes. In addition, since radar is an active sensor (i.e., it operates by transmitting signals and evaluating their reflections) interference becomes a crucial issue as the number of automotive radar sensors increases. This article gives an overview of the challenges that arise for automotive radar from its development as a sensor for ADAS to a core component of self-driving cars. It summarizes the relevant research and discusses the following topics related to highperformance automotive radar systems: 1) shortcomings of the classical signal processing algorithms due to underlying fundamental assumptions and a signal processing framework that overcomes these limitations, 2) use of digital modulations for automotive radar, and 3) interference-mitigation methods that enable multiple radar sensors to coexist in conditions of increasing market penetration. The overview presented in this article shows that new paradigms arise as automotive radar transitions into a more powerful vehicular sensor, which provides a fertile research ground for further investigation.

An MIMO Radar System Based on the Sparse-Array and Its Frequency Migration Calibration Method

In this paper, a Multiple Input Multiple Output (MIMO) radar system based on a sparse-array is proposed. In order to reduce the side-lobe level, a genetic algorithm (GA) is used to optimize the array arrangement. To reduce the complexity of the system, time-division multiplexing (TDM) technology is adopted. Since the signals are received in different periods, a frequency migration will emerge if the target is in motion, which will lead to the lower direction-of-arrival (DOA) performance of the system. To solve this problem, a stretching transformation method in the fast-frequency slow-time domain is proposed, in order to eliminate frequency migration. Only minor adjustments need to be implemented for the signal processing, and the root-mean-square error (RMSE) of the DOA estimation will be reduced by about 90%, compared with the one of an uncalibrated system. For example, a uniform linear array (ULA) MIMO system with 2 transmitters and 20 receivers can be replaced by the proposed system with 2 transmitters and 12 receivers, achieving the same DOA performance. The calibration formulations are given, and the simulation results of the automotive radar system are also provided, which validate the theory.

Cascaded Kalman filter for target tracking in automotive radar

In automotive anti-collision radar system, the accuracy of velocity is one of the key indicators to measure radar performance. For frequency modulation continuous wave radar with chirp sequence, which is commonly used in automotive radar, there are two problems that are velocity ambiguity and measurement error. In order to address these problems, a cascaded Kalman filter algorithm is proposed in this article, which is able to overcome velocity ambiguity and improve the accuracy of measurements. In addition, the problem of velocity ambiguity can be solved just in the data processing and it avoids transmitting staggered or multiple pulse repetition frequency signal. The simulation results show that this algorithm can effectively improve the tracking performance.

Statistical Characteristic-Based Road Structure Recognition in Automotive FMCW Radar Systems

This paper proposes an efficient road structure recognition method using statistical characteristics of received signals in automotive frequency-modulated continuous wave radar systems. Generally, roads consist of various structures, some of which, such as tunnels and soundproof walls made of iron, generate undesired echoes, called clutter. When the clutter flows into the radar system, the target detection performance cannot be guaranteed completely. This causes great danger to the driver using the radar function such as adaptive cruise control. Thus, an efficient method to recognize the structures that deteriorate the radar detection performance is desired. Depending on the types of road structures, frequency components of the received signals have distinctive distributions. Focusing on this point, parameters that reflect statistical properties of each distribution are extracted. These parameters can be used as standards for the recognition because they show different values according to the road structures. For more enhanced recognition, we use a support vector machine method with a linear classifier or a Gaussian kernel, and the resulting confusion matrices are derived. According to the results, the proposed method successfully classifies the structures with high accuracy. If the recognition of the road structures that degrade radar’s function is performed effectively, the safety of the driver in the radar-equipped vehicle can be ensured by applying additional signal processing or giving a warning message to the driver.

Performance comparison of sparse array millimeter wave imager configurations.

Millimeter wave imaging systems are a promising candidate for several applications such as indoor security, industrial non-destructive evaluation, and automotive radar systems. In this paper, we compare the performance of various array configurations that can be enabled by recent automotive radar chips, for imaging applications. High resolution real-time imaging requires an extensive number of measurements which demands a large number of emitters and receivers. Hence, cost and size become major considerations in the design process. In an attempt to reduce the number of emitter and receiver elements, we compare various antenna array architectures to optimize the hardware implementation for high resolution imaging. We consider mono-static single-input-single-output (SISO), multi-static multiple-input-multiple-output (Full-MIMO), and hybrid localized MIMO-SISO (Local-MIMO) architectures. The computationally reconstructed image quality and point spread function from each architecture are compared and traded for the system engineering complexity and cost. We present measurement results from a Synthetic Aperture Radar (SAR) system based on an automotive radar sensor to ensure it is representative of the system's physics. The comparative results of the SISO, Full-MIMO and Local-MIMO simulations provide for affordable alternatives to the high cost SISO approach.

Modulation type classification of interference signals in automotive radar systems

As the importance of radar sensors for automotive vehicles has rapidly increased, the mutual interference between vehicle radars has become important. If two or more radars share the same transmitting frequency band, their interference effects are retained after low-pass filtering. These interference signals can degrade the performance of target estimation, as they increase the noise floor level and create ghost targets, consequently causing false alarms. Several studies on mitigating interference effects have been conducted. However, they only focused on suppression techniques. For a complete interference mitigation algorithm, the identification of the existence of interference signals and classification of their modulation techniques should precede the mitigation steps. Therefore, the authors propose a method for the identification and the classification of interference signals. They modelled five different radar modulation signals as interference signals, and extracted five features from the time-domain mixer output signals. The training of the classification model and validation of its classification performance is done using the support vector machine. The simulation results show that the existence of interference signals is identified with the accuracy of over 99%. Furthermore, the interference signals are effectively classified into their modulation types with an accuracy of over 96%.

Road structure classification through artificial neural network for automotive radar systems

This study proposes an artificial neural network-based method to classify road structures for automotive radar systems. Generally, road structures generate unwanted echoes called clutter, which degrades the performance of target detection, and may cause critical errors in autonomous driving modes. However, recognition and classification of each individual structure type is very difficult, because there are various types of structures made by different materials which cause ambiguity in the recognition and classification process. To deal with these classification ambiguities, the authors propose an artificial neural network-based recognition and classification method. Road structures are classified by applying artificial neural network to the frequency domain-received signals of automotive radar systems. Once the neural network has been trained with the received signals, it is used to determine the type of road structure with instantaneous received signals. The classification performance was evaluated by experimenting with the number of antenna elements and radar snapshots. In addition, to find the most suitable artificial neural network structure, the authors experimented with the number of nodes and layers. After completing a period of deep learning using actual experimental data, the total classification accuracy was about 95%.

Mutual Interference Suppression and Signal Restoration in Automotive FMCW Radar Systems

Mutual Interference Suppression and Signal Restoration in Automotive FMCW Radar Systems