Automotive Radar Research Articles

A Modulated Approach for Improving MFSK RADARS to Resolve Mutual Interference on Autonomous Vehicles (AVs).

This paper proposes a novel automotive radar waveform involving the theory behind M-ary frequency shift key (MFSK) radar systems. Along with the MFSK theory, coding schemes are studied to provide a solution to mutual interference. The proposed MFSK waveform consists of frequency increments throughout the range of 76 GHz to 81 GHz with a step value of 1 GHz. Instead of stepping with a fixed frequency, a triangular chirp sequence allows for static and moving objects to be detected. Therefore, automotive radars will improve Doppler estimation and simultaneous range of various targets. In this paper, a binary coding scheme and a combined transform coding scheme used for radar waveform correlation are evaluated in order to provide unique signals. AVs have to perform in an environment with a high number of signals being sent through the automotive radar frequency band. Efficient coding methods are required to increase the number of signals that are generated. An evaluation method and experimental data of modulated frequencies as well as a comparison with other frequency method systems are presented.

An Improved Performance Radar Sensor for K-Band Automotive Radars.

This paper presents a new radar sensor configuration of a planar grid antenna array (PGAA) for automotive ultra-wideband (UWB) radar applications. For system realisation, the MIMO concept is adopted. The proposed antenna is designed to operate over the 24 GHz frequency band. It is based on split-ring resonator (SRR) elements to enhance the operating bandwidth and increase the antenna gain, leading to a better-performing radar system. The PGAA consists of thirty-one radiating elements, in which each element excitation is obtained using a common transmission line centre fed by a 50 Ω coaxial probe. By introducing a superstrate dielectric layer at a distance of λ/2 from the top of the antenna array, the PGAA gain and impedance bandwidth are further improved. The gain is improved by 2.7 dB to reach 16.5 dBi at 24 GHz, and the impedance bandwidth is enhanced to 9.3 GHz (37.7%). The measured impedance bandwidth of the proposed antenna array ranges from 20 GHz to 29.3 GHz for a reflection coefficient (S11) of less than -10 dB. The proposed antenna is validated for automotive applications.

Detection of Optical Road Markings With Automotive Radar Sensors

Detection of Optical Road Markings With Automotive Radar Sensors

Automotive Radar Optimization Design in a Spectrally Crowded V2I Communication Environment

A main challenge for radar-aided millimeter wave (mmWave) vehicle-to-infrastructure (V2I) communication application, is that it requires mitigating the mutual interference between vehicular radar and communication operating at same frequency bands. This paper considers the joint design of the multiple-input multiple-output (MIMO) transmit waveform and receive filter bank for road side unit (RSU)-mounted radar in a spectrally crowded V2I communication environment. With the criterion of maximizing the average signal-to-interference-plus-noise ratio (SINR), a non-convex problem, which involves the weighted-sum waveform energy over the overlayed space-frequency bands, energy and similarity constraints, is formulated. An iterative algorithm is proposed to solve the joint optimization problem. At each iteration, the transmit waveform is optimized based on the alternating direction method of multipliers (ADMM) method with a significantly lower computational complexity. As a consequence, accurate location information derived from the optimized radar is used to reduce the beam training overhead of V2I communication links. Finally, simulation results display the effectiveness of the devised method in finding feasible and enhanced solutions, importantly outperforming several counterparts.

Contributions to Unsupervised Online Misalignment Detection and Bumper Error Compensation for Automotive Radar.

One of the fundamental sensors utilized in the Advanced Driver Assist System (ADAS) is the radar sensor. Automotive-related functions need highly precise detection and range of traffic and surroundings; otherwise, the whole ADAS performance suffers. The radar placement beneath a bumper or a cover, the age or exposure to accidents or vehicle vibration, vehicle integration, and mounting tolerances will impact the angular performance of the radar sensor. In this research, we present an unsupervised online method for elevation mounting angle error compensation and a method for bumper and environmental error compensation in the azimuth direction. The proposed methods need no specific calibration jig and may be used to replace traditional initial calibration methods; they also enable ongoing calibration throughout the sensor's lifespan. A first proposed standalone method for vertical alignment uses stationary radar targets reflected from the environment to calculate a vertical misalignment angle with a line-fitting algorithm. The vertical mounting error compensation approach delivers two types of correction values: a dynamic value that converges quickly in the case of minor accidents and a more stable correction value that converges slowly but offers a long-term compensation value over the sensor's lifespan. A second proposed solution uses the vehicle velocity and radar targets properties, like relative velocity and measured azimuth angle, to calculate an individual azimuth correction curve. Real-world data collected from drive testing with a 77 GHz series automobile radar was used to analyze the performance of the proposed methods, yielding encouraging results.

A novel reconfigurable H-plane Horn leaky wave Substrate Integrated Waveguide MIMO antenna for K band

In this paper a novel reconfigurable H-plane horn Multiple Input Multiple Output (MIMO) leaky wave substrate integrated waveguide (SIW) antenna is proposed. The antenna is operating at two resonant frequencies, 22.8 GHz and 26.32 GHz. The transverse periodic leaky wave slots are radiating power in the broadside direction at 22.8 GHz and pattern diversity is achieved at 26.32 GHz. Dielectric loading and an array of meta-material structures are used, which further improve gain of the antenna. The antenna achieves better matching with a return loss of –33.5 dB at 22.8 GHz and maximum isolation of −38.7 dB at 26.32 GHz is achieved between antenna elements. A 2x2 and 4x4 MIMO SIW antenna is designed and analyzed in this paper. All the MIMO performance measurement parameters have values within limits. The antenna achieves a maximum gain of 7.3 dBi and 8.1 dBi. The proposed antenna operates in the K band (18 GHz–27 GHz) region, making it suitable for automotive radars and short-range applications.

A Bi-Objective Simulation Facility for Speed and Range Calibration of 24 GHz and 77 GHz Automotive Millimeter-Wave Radars for Environmental Perception

Automotive millimeter-wave radars are widely used to measure kinematic parameters of surrounding objects in the environmental perception of automated vehicles and typically operate in frequency bands of around 24 GHz and 77 GHz. The 24 GHz and 77 GHz automotive millimeter-wave radars are similar in their speed and range measurement principles; however, they are distinct in their accuracies when measuring the above kinematic parameters because of the different carrier frequencies and bandwidths. In order to evaluate the kinematic parameter measurement performances of automotive millimeter-wave radars for environmental perception, this paper proposes a calibration method and establishes a bi-objective simulation facility for the speed and range calibration of 24 GHz and 77 GHz automotive millimeter-wave radars. The feasibility of the proposed calibration method and the accuracy of the established bi-objective simulation facility are verified by the results obtained via experiments conducted on two 24 GHz and 77 GHz millimeter-wave radar samples. According to the speed and range calibration results of the two millimeter-wave radar samples, the 77 GHz millimeter-wave radar sample possesses superior speed measurement accuracy compared with the 24 GHz sample. Additionally, the range measurement errors of the 77 GHz millimeter-wave radar sample are much smaller than those of the 24 GHz sample. This indicates that the 77 GHz millimeter-wave radar sample is more accurate and reliable than the 24 GHz sample. Finally, the 77 GHz millimeter-wave radar sample was tested for its speed and range resolving ability. The results confirm that the bi-objective simulation facility can satisfy the calibration requirements of speed resolution up to 0.01 m/s and range resolution up to 0.1 m. The proposed calibration method and the established simulation facility can easily satisfy the speed, range and resolution calibration requirements of 24 GHz and 77 GHz millimeter-wave radars with high precision and wide range, and can achieve more economical, comprehensive and traceable performance testing on both 24 GHz and 77 GHz automotive millimeter-wave radars for environmental perception.

Channel Measurement and Ray-Tracing Simulation for 77 GHz Automotive Radar

Millimeter-wave automotive radar is essential for realizing autonomous driving. Realistic channel model and simulation are important for system and sensing algorithm design. This work introduces channel measurements and ray-tracing (RT) channel simulations for frequency modulated continuous wave (FMCW) automotive mmWave radar. The 77 GHz channel measurements in an urban crossroads environment are presented. The dominant echoes (multi-path components) of the measurement are detected and matched with corresponding objects for each frame. A measurement-based electromagnetic (EM) parameter estimation method is proposed to find the optimal parameter set that minimizes the error of simulated radar cross section (RCS). As a result, the issue of lacking reliable EM material parameters for RT simulation at 77 GHz frequency band is tackled. Simulation results are validated in power, range, velocity, and angle domains with the provided EM parameter set. Extending validated RT simulations in similar environments with various configurations allows more reliable channels for rigorous testing without the limitation of channel measurement.

Performance Analysis of Coordinated Interference Mitigation Approach for Automotive Radar

Millimeter automotive radar has great potential in Advanced Driver Assistance Systems (ADAS) to enable safety features such as adaptive cruise control and collision avoidance. However, with widely deployment of millimeter radars on vehicles, the risk of radar mutual interference becomes a major factor limiting the high performance of radar detection. In this paper, we analyze the mutual interference among multiple Frequency Modulated Continuous Wave (FMCW) radars. On one hand, we study the interference in detail by considering co-channel interference and adjacent channel interference simultaneously. Besides, the co-channel interference is analyzed by employing stochastic geometry model while the adjacent channel interference is assessed by the deterministic analysis method. On the other hand, we propose a Time-Frequency Division Multiple Access (TFDMA) scheme to mitigate the interference in a coordinated manner and evaluate it in terms of mitigation delay, the probability of interference, effective detectable density, maximum number of interference-free radar, and control signaling overhead. Finally, we study the power allocation strategy to enable the effectiveness of the coordinated interference mitigation approach based on the interference analysis. Simulation results verify the proposed framework for interference analysis by employing Monte Carlo method, and the performance improvement of the coordinated interference mitigation approach is 3.5 dB.

4DEgo: ego-velocity estimation from high-resolution radar data

Automotive radars allow for perception of the environment in adverse visibility and weather conditions. New high-resolution sensors have demonstrated potential for tasks beyond obstacle detection and velocity adjustment, such as mapping or target tracking. This paper proposes an end-to-end method for ego-velocity estimation based on radar scan registration. Our architecture includes a 3D convolution over all three channels of the heatmap, capturing features associated with motion, and an attention mechanism for selecting significant features for regression. To the best of our knowledge, this is the first work utilizing the full 3D radar heatmap for ego-velocity estimation. We verify the efficacy of our approach using the publicly available ColoRadar dataset and study the effect of architectural choices and distributional shifts on performance.

Mitigation of Millimeter-Wave Radar Mutual Interference Using Spectrum Sub-Band Analysis and Synthesis

Millimeter-wave radars are widely used in automotive radars because of their all-weather and all-day operation capability. However, as more and more radar sensors are used, the possibility of mutual interference between radars increases dramatically. Severe interference increases the noise level, affects target detection performance, and can lead to missed detection and wrong detection. In this study, a novel solution to the problem of mutual radar interference is introduced. The method is based on the analysis and synthesis of spectrum sub-bands. Specifically, the received radar signal is partitioned into sub-bands, after which interference mitigation is carried out in each sub-band. Finally, the signals are reconstructed to obtain interference-free data. The effectiveness of this approach is evaluated using both a simulated multi-target scenario and a real-life experimental environment. The results demonstrate that the proposed method outperforms existing techniques in terms of interference mitigation while exhibiting rapid processing speeds.

Design of dual-band planar thin absorber for vehicular radar test scenario

ABSTRACT Aiming at the requirement of radar wave absorption in automotive radar tests for both the 24 GHz and 77 GHz vehicle radar bands, two ultra-thin dual-band circuit analog absorber designs based on frequency-selective conductive film have been proposed. They are obtained by first optimising the equivalent circuit parameters to achieve the desired reflection response, then determining the physical parameters (geometrical size and square resistance) of unit cells from the equivalent circuit parameters, and finally fine-tuning these physical parameters. Both designs employ unit cell based on the nested structure of conductive square ring and a conductive square patch, which has the advantages of simple structure, ease of processing, high design freedom, and good polarisation and angular stability. The 1st design employs different square resistance values for the conductive ring and patch, while the 2nd design employs a single square resistance value for both conductive shapes. Simulation results show that, for each of the two designs, the lower and upper 70% absorption or −10 dB reflection bands simultaneously cover the 24 GHz and 77 GHz vehicle radar bands, respectively, with good angular stability. The dual-band coverage keeps stable until the elevation incident angle reaches 20° and 50° for the 1st and 2nd designs, respectively. A realistic sample is processed regarding the 2nd design and its dual-band absorption performance has been verified by experimental results, which have good agreement with the simulated counterparts.

Metaheuristic for Optimal Dynamic K-Coloring Application on Band Sharing for Automotive Radars

The number of vehicles equipped with radars on the road has been increasing for years and is expected to reach 50% of cars by 2030. This rapid rise in radars will likely increase the risk of harmful interference, especially since radar specifications from standardization bodies (e.g., ETSI) provide requirements in terms of maximum transmit power but do no mandate specific radar waveform parameters nor channel access scheme policies. Techniques for interference mitigation are thus becoming very important to ensure the long-term correct operation of radars and upper-layer ADAS systems that depend on them in this complex environment. In our previous work, we have shown that organizing the radar band into time-frequency resources that do not interfere with each other vastly reduces the amount of interference by facilitating band sharing. In this paper, a metaheuristic is presented to find the optimal resource sharing between radars, knowing their relative positions and thereby the line-of-sight and non-line-of-sight interference risks during a realistic scenario. The metaheuristic aims at optimally minimizing interference while minimizing the number of resource changes that radars have to make. It is a centralized approach where everything about the system is known (e.g., the past and future positions of the vehicles). This and the high computational load induce that this algorithm is not meant to be used in real-time. However, the metaheuristic approach can be extremely useful for finding near optimal solutions in simulations, allowing for the extraction of efficient patterns, or as data generation for machine learning.

Road-Map Aided Gaussian Mixture Labeled Multi-Bernoulli Filter for Ground Multi- Target Tracking

Ground multi-target tracking (MTT) is one of the core tasks of airborne ground moving target indicator (GMTI) radar and automotive radar. However, ground MTT still remains a challenging issue especially in complex traffic scenarios, since it often suffers from high clutter, dense targets, low visibility, etc. To enhance the tracking performance for ground multiple targets, in this paper, we present a comprehensive solution named road-map aided Gaussian mixture labeled multi-Bernoulli filter (RA-GMLMB) filter, which incorporates road-map information into the labeled multi-Bernoulli (LMB) filter. Specifically, we first propose a hybrid circular arc and line segments approximation approach to extract road-map information, which alleviates approximation errors in the procedure of road-map approximation. Then, we deduce the RA-GMLMB filter by integrating the extracted road-map information into the LMB filter with Gaussian mixture implementation. Simulation experiments are conducted and experimental results show that the proposed RA-GMLMB filter outperforms the state-of-the-art methods.

Automotive Radar Modeling for Virtual Simulation Based on Mixture Density Network

Road safety is the fundamental purpose of advanced driver assistance systems (ADAS), and automotive Radar often plays a significant role in reliable environment perception. Therefore, as the complexity of system integration continues to increase, the development quality and speed become increasingly crucial. Nowadays, sensor virtualization helps expedite the development process. Since Radar must perform several measurements in a short period to resolve ambiguity, the resulting radar signal is multi-dimensional and data-intensive. Therefore, analyzing these signals and generating models from them are not an easy task. To overcome the challenges, we present a Radar model based on Mixture Density Network (MDN) to generate production sensor errors that exhibit varied input correlations. Meanwhile, the errors modify a specific object list of Radar during the virtualized sensing process. The results show that MDN networks can better express the uncertainty of sensor detection errors and can be generalized to a generic sensor model. Finally, the MDN-based radar model is integrated into a multi-body simulation virtual platform with an open simulation interface to validate its performance.

Three More Decades in Array Signal Processing Research: An optimization and structure exploitation perspective

The signal processing community is currently witnessing the emergence of sensor array processing and direction-of-arrival (DoA) estimation in various modern applications, such as automotive radar, mobile user and millimeter wave indoor localization, and drone surveillance, as well as in new paradigms, such as joint sensing and communication in future wireless systems. This trend is further enhanced by technology leaps and the availability of powerful and affordable multiantenna hardware platforms.

Binary-Phase vs. Frequency Modulated Radar Measured Performances for Automotive Applications.

Radars have been widely deployed in cars in recent years, for advanced driving assistance systems. The most popular and studied modulated waveform for automotive radar is the frequency-modulated continuous wave (FMCW), due to FMCW radar technology's ease of implementation and low power consumption. However, FMCW radars have several limitations, such as low interference resilience, range-Doppler coupling, limited maximum velocity with time-division multiplexing (TDM), and high-range sidelobes that reduce high-contrast resolution (HCR). These issues can be tackled by adopting other modulated waveforms. The most interesting modulated waveform for automotive radar, which has been the focus of research in recent years, is the phase-modulated continuous wave (PMCW): this modulated waveform has a better HCR, allows large maximum velocity, permits interference mitigation, thanks to codes orthogonality, and eases integration of communication and sensing. Despite the growing interest in PMCW technology, and while simulations have been extensively performed to analyze and compare its performance to FMCW, there are still only limited real-world measured data available for automotive applications. In this paper, the realization of a 1 Tx/1 Rx binary PMCW radar, assembled with connectorized modules and an FPGA, is presented. Its captured data were compared to the captured data of an off-the-shelf system-on-chip (SoC) FMCW radar. The radar processing firmware of both radars were fully developed and optimized for the tests. The measured performances in real-world conditions showed that PMCW radars manifest better behavior than FMCW radars, regarding the above-mentioned issues. Our analysis demonstrates that PMCW radars can be successfully adopted by future automotive radars.

Road User Detection on Polarimetric Pre-CFAR Radar Data Level

Perceiving the road environment as robust and complete as possible is a fundamental requirement on the way to fully automated driving. Crucial components of perception include detection and classification of road users as well as estimating their extensions and orientation. Using data from a newly available automotive polarimetric radar, this work presents a neural network model using pre-CFAR data as input to detect road users and additionally predict their oriented bounding box. The model is trained to detect static and dynamic road users in diverse urban and rural scenarios on a large data set. A major improvement in detection performance is shown when comparing against CFAR based detection approaches. Additionally, the benefit of polarimetric information is evaluated by optimizing the model on two representations of polarimetric information and comparing it to a model on data without polarimetric information. Results show further promising performance increases when polarimetric information is available.

Deep Learning-Based Link Configuration for Radar-Aided Multiuser mmWave Vehicle-to-Infrastructure Communication

Configuring millimeter wave links following a conventional beam training protocol, as the one proposed in the current cellular standard, introduces a large communication overhead, especially relevant in vehicular systems, where the channels are highly dynamic. In this paper, we propose the use of a passive radar array to sense automotive radar transmissions coming from multiple vehicles on the road, and a radar processing chain that provides information about a reduced set of candidate beams for the links between the road-infrastructure and each one of the vehicles. This prior information can be later leveraged by the beam training protocol to significantly reduce overhead. The radar processing chain estimates both the timing and chirp rates of the radar signals, isolates the individual signals by filtering out interfering radar chirps, and estimates the spatial covariance of each individual radar transmission. Then, a deep network is used to translate features of these radar spatial covariances into features of the communication spatial covariances, by learning the intricate mapping between radar and communication channels, in both line-of-sight and non-line-of-sight settings. The communication rates and outage probabilities of this approach are compared against exhaustive search and pure radar-aided beam training methods (without deep learning-based mapping), and evaluated on multi-user channels simulated by ray tracing. Results show that: (i) the proposed processing chain can reliably isolate the spatial covariances for individual radars, and (ii) the radar-to-communications translation strategy based on deep learning provides a significant improvement over pure radar-aided methods in both LOS and NLOS channels.

Optimum Radome Design of Automotive Radar for Multi-Function and Long Range

Optimum Radome Design of Automotive Radar for Multi-Function and Long Range