Automotive Radar Research Articles

A dualband bandpass filter with tunable bandwidths for automotive radar and 5G millimeter-wave applications

A compact dual wideband bandpass filter for automotive radar (AR) and 5G millimeter-wave (mmWave) applications with adjustable bandwidths is presented in this work. The filter is based on microstrip dual-mode edge-coupled stub-loaded resonators (SLR). The novelty of this configuration is that it allows independent control of the bandwidths which is not obvious for many reported SLR-based filters. To achieve an overall size of 1.23λ_g× 2.02λ_g, these resonators are coupled and bent int o U-shape, T-shape, and E-shape. The U-shaped resonator, based on a quarter-wavelength transmission line, is coupled to the T-shaped element which constitutes the main feed line to obtain the dual-band response. Having a symmetrical structure, the design i s studied using the even-odd mode analysis. The layout is made using the Ansys high-frequency structural simulator (HFSS) and fabricated on Rogers RO3010 with a thickness of 1.28 mm, a dielectric constant of 10.2, and a loss tangent of 0.0022. The measured bandwidths are 660 MHz and 880 MHz at central frequencies of 23.92 GHz and 28.38 GHz respectively. The measured insertion losses are less than 3.9 dB and the return losses are greater than 17 dB in both bands. A good agreement is obtained between simulate d and experimental results.

Radar Scheme With Raised Reflector for NLOS Vehicle Detection

The employment of passive reflectors enables the millimeter-wave automotive radars to detect an approaching vehicle in non-line-of-sight conditions. In this paper, the installation of such reflectors above the sidewalk at an intersection is proposed and studied, avoiding pedestrians’ blockage and road dust effect at ground level. Through the analysis of the backscattering power, it is shown that the suggested scheme may detect an approaching vehicle in the blind zone at distances of 30…50 m to the intersection point. Additionally, the analysis shows that efficient operation is highly dependent on the spatial orientation and size of the reflector. Even a few degrees rotation may change the detecting range by several meters. In turn, the larger area of the reflector may cover longer detecting distances, improving the radar scheme’s overall performance. It is also shown that further performance enhancement can be achieved by employing a C-type radar, contributing an extra 5 dB to the backscattering power relative to an A-type radar. However, despite these improvements, the strongest scattering centre of the detectable vehicle is systematically identified to the bumper zone.

A Credible and Robust Approach to Ego-Motion Estimation Using an Automotive Radar

Consistent motion estimation is fundamental for all mobile autonomous systems. While this sounds like an easy task, often, it is not the case because of changing environmental conditions affecting odometry obtained from vision, Lidar, or the wheels themselves. Unsusceptible to challenging lighting and weather conditions, radar sensors are an obvious alternative. Usually, automotive radars return a sparse point cloud, representing the surroundings. Utilizing this information to motion estimation is challenging due to unstable and phantom measurements, which result in a high rate of outliers. We introduce a credible and robust probabilistic approach to estimate the ego-motion based on these challenging radar measurements; intended to be used within a loosely-coupled sensor fusion framework. Compared to existing solutions, evaluated on the popular nuScenes dataset and others, we show that our proposed algorithm is more credible while not depending on explicit correspondence calculation.

A Novel 65 nm Active-Inductor-Based VCO with Improved Q-Factor for 24 GHz Automotive Radar Applications.

The inductor was primarily developed on a low-voltage CMOS tunable active inductor (CTAI) for radar applications. Technically, the factors to be considered for VCO design are power consumption, low silicon area, high frequency with reasonable phase noise, an immense quality (Q) factor, and a large frequency tuning range (FTR). We used CMOS tunable active inductor (TAI) topology relying on cascode methodology for 24 GHz frequency operation. The newly configured TAI adopts the additive capacitor with the cascode approach, and in the subthreshold region, one of the transistors functions as the TAI. The study, simulations, and measurements were performed using 65nm CMOS technology. The assembled circuit yields a spectrum from 21.79 to 29.92 GHz output frequency that enables sustainable platforms for K-band and Ka-band operations. The proposed design of TAI demonstrates a maximum Q-factor of 6825, and desirable phase noise variations of −112.43 and −133.27 dBc/Hz at 1 and 10 MHz offset frequencies for the VCO, respectively. Further, it includes enhanced power consumption that varies from 12.61 to 23.12 mW and a noise figure (NF) of 3.28 dB for a 24 GHz radar application under a low supply voltage of 0.9 V.

A Two-Stage DNN Model With Mask-Gated Convolution for Automotive Radar Interference Detection and Mitigation

As the number of radar sensors on the road increases rapidly and many of these sensors share the same frequency spectrum, mutual interference cannot be avoided. This paper introduces a novel automotive radar interference mitigation approach using an autoencoder model which consists of separate neural networks for the detection and reconstruction steps. A mask-gated convolution is proposed to help the reconstruction neural network to learn the signal pattern from interference-free samples and to interpolate accordingly the signal segments at the disturbed positions. Through perturbation analysis it is shown that the reconstruction neural network can recover the distorted samples by utilizing their surrounding relevant samples. By exploiting the nature of interference in real-world scenarios, the proposed training approach does not need hand-labeled training data. Together with the proposed composite training loss, the neural network can recover the disturbed discrete beat signal with remarkable improvements in the signal-to-interference-plus-noise ratio (SINR) and the mean absolute percentage error (MAPE). Moreover, despite the use of a purely simulated training data set, the autoencoder can deal with real-world radar measurements which are more complex than the training data set.

Adaptive Waveform Design for Communication-Enabled Automotive Radars

Large-scale deployment of connected vehicles with cooperative sensing technologies increases the demand on the vehicular communication spectrum in 5.9 GHz allocated for the exchange of safety messages. To support the high data rates needed by such applications, the millimeter-wave (mmWave) automotive radar spectrum at 76–81 GHz can be utilized for wideband communication as well. For this purpose, various joint automotive radar-communication (JARC) systems have been proposed in the literature to perform both functions using the same wideband waveform. However, the wideband joint waveforms encounter frequency-selectivity in both radar and communication channels due to multi-path propagation. In this paper, we address the optimal joint waveform design problem to exploit the frequency-selectivity for wideband JARC operations via orthogonal frequency-division multiplexing (OFDM) wherein subcarrier coefficients are designed for optimal power allocation and phase coding. We show that the problem is a non-convex quadratically constrained quadratic programming (QCQP) problem which is known to be NP-hard. Existing approaches to solve QCQP include semidefinite relaxation (SDR) which incurs high time complexity. Instead, we propose approximation methods to solve QCQP more efficiently by leveraging structured matrices and using convex approximations. Finally, we demonstrate the efficacy of the proposed approaches through numerical simulations.

Radar Detection in Vehicular Networks: Fine-Grained Analysis and Optimal Channel Access

Automotive radar is a critical feature in advanced driver-assistance systems. It is important in enhancing vehicle safety by detecting the presence of other vehicles in the vicinity. The performance of radar detection is, however, affected by the interference from radars of other vehicles as well as the variation in the target radar cross-section (RCS) due to varying physical features of the target vehicle. Considering such interference and random RCS, this work provides a fine-grained performance analysis of radar detection. Specifically, using stochastic geometry, we calculate the meta distribution of the signal-to-interference-and-noise ratio that permits the reliability analysis of radar detection at individual vehicles. We also evaluate the delay aspect of radar detection, namely, the mean local delay which is the average number of transmission attempts needed until the first successful target detection. For a given target distance, we obtain the optimal transmit probability that maximizes the density of successful detection while keeping the mean local delay below a threshold. We also provide several system design insights in terms of the fraction of reliable radar links, transmission delay, the density of vehicles, and congestion control. Finally, leveraging the derived framework, we present an online algorithm to select the transmit probability in a distributed manner, particularly when system parameters such as vehicular density and interferer locations are unknown to individual vehicles. We demonstrate the efficacy of the proposed online framework for selecting the optimal transmit probability within a deadline of 0.1 ms, which is critical for sustaining automotive safety applications and services.

A 77-GHz Low-Conversion-Loss Fourth-Harmonic Mixer on SISL Platform

This letter presents a 77-GHz low-conversion-loss (CL) fourth-harmonic mixer based on the substrate-integrated suspended line (SISL) platform. Based on the traditional stub-type harmonic mixing structure, we add several additional transmission lines, which will participate in the impedance matching of the diode together with stubs to reduce the CL. A 3-D electromagnetic (EM) diode model is established for accurate simulation at millimeter-wave frequency. The presented SISL mixer was fabricated by the low-cost printed circuit board (PCB) process and was self-packaged in FR4 substrates. When the local oscillator (LO) frequency is 19.45 GHz and the radio frequency (RF) is 78 GHz, the minimum CL is 12.2 dB under an LO input power of 12 dBm. This mixer is suitable for 77-GHz automotive anti-collision radar systems.

A Broadband E-Band Single-Layer-SIW-to-Waveguide Transition for Automotive Radar

An <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$E$ </tex-math></inline-formula> -band air-filled rectangular waveguide (RWG) to single thin-layer (0.127 mm) substrate integrated waveguide (SIW) transition is proposed for automotive radar applications. The compact transition structure is realized using a symmetric narrow aperture coupled comb patches. With the mutual coupling effect between patches, a bandwidth greater than 22% for <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$|S_{11}| &lt; -15$ </tex-math></inline-formula> dB is achieved. A back-to-back prototype is fabricated and measured. The results show that the insertion loss of a single transition is less than 0.5 dB within 71–86 GHz, which covers the whole attractive frequency range for automotive radar. With broadband and compact size, the proposed transition presents potential for automotive radar.

CMOS Automotive Radar Sensors: mm-Wave Circuit Design Challenges

This brief gives an overview of recent advancements in CMOS automotive radar sensors, focusing on the most critical circuit design at mm-wave frequencies. Highly scaled CMOS technologies enable very low-power consumption, reduced costs, and higher data processing capacity and integration compared to state-of-the-art SiGe BiCMOS chip-set solutions commonly adopted in commercial products. The very bottleneck is still represented by the radio front-end due to the challenging requirements of long-range radars. Circuit topologies and design tradeoffs are here discussed with reference to critical building blocks of the mm-wave radar front-end, reporting circuit implementations for a 77-GHz long-range radar sensor in a 28-nm fully-depleted silicon-on-insulator CMOS technology.

Towards Deep Radar Perception for Autonomous Driving: Datasets, Methods, and Challenges.

With recent developments, the performance of automotive radar has improved significantly. The next generation of 4D radar can achieve imaging capability in the form of high-resolution point clouds. In this context, we believe that the era of deep learning for radar perception has arrived. However, studies on radar deep learning are spread across different tasks, and a holistic overview is lacking. This review paper attempts to provide a big picture of the deep radar perception stack, including signal processing, datasets, labelling, data augmentation, and downstream tasks such as depth and velocity estimation, object detection, and sensor fusion. For these tasks, we focus on explaining how the network structure is adapted to radar domain knowledge. In particular, we summarise three overlooked challenges in deep radar perception, including multi-path effects, uncertainty problems, and adverse weather effects, and present some attempts to solve them.

Contactless Soil Moisture Mapping Using Inexpensive Frequency-Modulated Continuous Wave RADAR for Agricultural Purposes

Soil Moisture (SM) is one of the most critical factors for a crop’s growth, yield, and quality. Although Ground-Penetrating RADAR (GPR) is commonly used in satelite observation to analyze soil moisture, it is not cost-effective for agricultural applications. Automotive RADAR uses the concept of Frequency-Modulated Continuous Wave (FMCW) and is more competitive in terms of price. This paper evaluates the viability of using a cost-effective RADAR as a substitute for GPR for soil moisture content estimation. The research consisted of four experiments, and the results show that the RADAR’s output signal and the soil moisture sensor SEN0193 have a high correlation with values as high as 0.93 when the SM is below 15%. Such results show that the tested sensor (and its cost-effective working principle) are able to determine soil water content (with certain limitations) in a non-intrusive, proximal sensing manner.

IRNet: Interference Recognition Networks for Automotive Radars via Autocorrelation Features

With the rapid increase of the number of autonomous driving vehicles, more and more radars working on a similar frequency band are equipped on vehicles, which incurs risks of automotive radar interference. Therefore, a precise interference recognition method is becoming a compelling task. In this article, a novel interference recognition network, called IRNet, is proposed to recognize eight types of interference signals that include mutual interference, unintended interference, and adverse intended interference. First, autocorrelation features of interference signals are obtained via calculating their autocorrelation functions (ACFs) and converted as two-dimensional feature images. Next, these images are employed as inputs of the IRNet that owns significant representation power and adaptive feature selection ability. Finally, the prediction types of interference will be output via the IRNet directly. The robustness of autocorrelation features is verified via simulations. The simulation results show that the proposed method can achieve 90.85% overall recognition accuracy when the jamming-to-noise ratio (JNR) is −14 dB and nearly 100% when JNR <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$&gt; -8$ </tex-math></inline-formula> dB. Compared with six state-of-the-art methods, the IRNet achieves much better recognition performance, especially under lower JNR conditions with relatively lower and acceptable computational complexity. Results on measured signals also verify the powerful generalization ability of the proposed method.

Cooperative Automotive Radars with Multi-Aperture Multiplexing MIMO Sparse Array Design

In this paper, a multi-aperture multiplexing multiple-input multiple-output (MAM-MIMO) sparse array is presented for cooperative automotive radars (CARs). The proposed sparse array composed of multiple subarrays can simultaneously cover a wide field-of-view (FOV) and achieve the required azimuth resolution at different ranges. To validate this idea, an optimization model for the MAM-MIMO sparse array is derived based on the example of CARs. This optimization model has been found by combining the peak-to-sidelobe ratio (PSLR) at all beams pointing within the constraints of different detection ranges. In addition, a hierarchical genetic algorithm based on the multi-objective decomposition method has been developed to obtain the optimized sparse array. The proposed method has been evaluated through both simulations and experiments. It is demonstrated that the optimized MAM-MIMO sparse array can effectively suppress sidelobes of its subarrays, yet with reasonably high azimuth resolutions and large FOVs.

Iterative 2D sparse signal reconstruction with masked residual updates for automotive radar interference mitigation

Compressive sensing has attracted considerable attention in automotive radar interference mitigation. However, these algorithms usually cannot be applied directly to commercial automotive radar as most of them are computationally intense. In this paper, we therefore introduce a computationally efficient two-dimensional masked residual updates (2D MRU) compressive sensing framework. By utilizing the sparsity of the beat signal in the frequency domain, the range-Doppler (RD) spectrum can be reconstructed with the help of undistorted samples in the beat signal. Unlike the other schemes, where a 2D signal measurement is vectorized into a 1D signal, the proposed 2D MRU can directly take a 2D signal measurement and reconstruct the corresponding RD spectrum. Furthermore, the 2D MRU framework can be easily integrated into well-known optimization schemes such as basis pursuit, iterative hard thresholding, iterative soft thresholding, orthogonal matching pursuit, and approximate message-passing algorithm. In addition to the standard iterative thresholding algorithms, we propose a novel prior-model-based iterative thresholding method to further reduce the computation time and reconstruction error. Theoretical analysis shows that the proposed framework can successfully reconstruct the RD spectrum with high probability. Moreover, numerical experiments demonstrate the superiority of the proposed framework in terms of computational complexity.

A Broadband 77/79 GHz Transmitter With Dual VCOs and Third Harmonic Signal Extraction in a 28 nm CMOS Technology

A broadband 77/79 GHz transmitter (TX) front-end for automotive long range radar (LRR) and short range radar (SRR) applications is presented in this paper. To achieve the best system performance one new TX architecture with two specifically designed voltage controlled oscillators (VCOs) is implemented in a 28 nm CMOS technology. Furthermore, the degradation of the VCO phase noise due to the TX integration is analyzed in detail and solutions to minimize the impacts are proposed and verified. Experimental results of a 20 GHz push-push VCO1 measured at the 77 GHz TX output show a continuous tuning range of 4.75 GHz, a coarse tuning range of 3.2 GHz and an average phase noise of −100 dBc/Hz @ 1 MHz, while a 26 GHz VCO2 with third harmonic signal extraction achieves a continuous and coarse tuning range of 7.5 GHz and 4.2 GHz with an average phase noise of −96 dBc/Hz @ 1 MHz at 79 GHz TX output. Moreover, a record pushing performance of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\rm {&lt;\!\pm }$</tex-math></inline-formula> 100 MHz/V at 77/79 GHz TX output has been achieved according to the authors’ best knowledge. The whole TX chip consumes about 860 mW from a single 2.1 V supply while providing more than 16 dBm output power over the whole frequency band.

Multi-Class Road User Detection With 3+1D Radar in the View-of-Delft Dataset

Next-generation automotive radars provide elevation data in addition to range-, azimuth- and Doppler velocity. In this experimental study, we apply a state-of-the-art object detector (PointPillars), previously used for LiDAR 3D data, to such 3+1D radar data (where 1D refers to Doppler). In ablation studies, we first explore the benefits of the additional elevation information, together with that of Doppler, radar cross section and temporal accumulation, in the context of multi-class road user detection. We subsequently compare object detection performance on the radar and LiDAR point clouds, object class-wise and as a function of distance. To facilitate our experimental study, we present the novel View-of-Delft (VoD) automotive dataset. It contains 8693 frames of synchronized and calibrated 64-layer LiDAR-, (stereo) camera-, and 3+1D radar-data acquired in complex, urban traffic. It consists of 123106 3D bounding box annotations of both moving and static objects, including 26587 pedestrian, 10800 cyclist and 26949 car labels. Our results show that object detection on 64-layer LiDAR data still outperforms that on 3+1D radar data, but the addition of elevation information and integration of successive radar scans helps close the gap. The VoD dataset is made freely available for scientific benchmarking at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://intelligent-vehicles.org/datasets/view-of-delft/</uri> .

High gain array antenna for 24 GHz FMCW automotive radars

This article describes two high gain array antennas for 24 GHz FMCW automotive radar. The bandwidth of the antenna is improved by applying the slot-loaded technique. To further enhance the gain characteristics, series-fed and corporate-fed planar array antennas are developed, and their performance is studied. The measurement results show that the proposed array antennas have an excellent performance over the 24 GHz ISM band. The proposed array antennas are suitable for 24 GHz FMCW automotive radar applications.

Automotive Radar Processing With Spiking Neural Networks: Concepts and Challenges.

Frequency-modulated continuous wave radar sensors play an essential role for assisted and autonomous driving as they are robust under all weather and light conditions. However, the rising number of transmitters and receivers for obtaining a higher angular resolution increases the cost for digital signal processing. One promising approach for energy-efficient signal processing is the usage of brain-inspired spiking neural networks (SNNs) implemented on neuromorphic hardware. In this article we perform a step-by-step analysis of automotive radar processing and argue how spiking neural networks could replace or complement the conventional processing. We provide SNN examples for two processing steps and evaluate their accuracy and computational efficiency. For radar target detection, an SNN with temporal coding is competitive to the conventional approach at a low compute overhead. Instead, our SNN for target classification achieves an accuracy close to a reference artificial neural network while requiring 200 times less operations. Finally, we discuss the specific requirements and challenges for SNN-based radar processing on neuromorphic hardware. This study proves the general applicability of SNNs for automotive radar processing and sustains the prospect of energy-efficient realizations in automated vehicles.

Near‐ground propagation in automotive radar and communication obstructed deployments: Measurements and modelling

Wireless communication and radars will play a crucial role for autonomous vehicles in the nearest future. However, the blockage caused by surrounding cars can degrade communication performance, while automotive radars are never aimed to operate in such conditions. Therefore, in this paper, the authors propose the concept of near-ground propagation, reducing the blockage effect in the road traffic conditions. Specifically, the radio waves may freely propagate under the blocking car's bottom if the antennas are placed as low as possible to the road. Based on the measured and modelled results presented in the paper, it may be claimed that near-ground communication and radar sensing are feasible and may combat even heavily obstructed cases. Nevertheless, some challenges associated with antenna locations were encountered. For example, it was discovered that antenna height at 0.5 m acts less effectively against blockage than at 0.3 m. Next, the 27 dB excess loss at the 0.5 m antenna height in the radar deployment is larger than 17 dB at 0.3 m. In its turn, the higher ground clearance of the blocking vehicle positively affects the near-ground performance. Additionally, the signal propagation at the grazing angle crucially reduces the relevant losses.