This paper investigates the effects of coarse quantization with mixed precision on measurements obtained from sparse linear arrays, synthesized by a collaborative automotive radar sensing strategy. The mixed quantization precision significantly reduces the data amount that needs to be shared from radar nodes to the fusion center for coherent processing. We utilize the low-rank properties inherent in the constructed Hankel matrix of the mixed-precision array, to recover azimuth angles from quantized measurements. Our proposed approach addresses the challenge of mixed-quantized Hankel matrix completion, allowing for accurate estimation of the azimuth angles of interest. To evaluate the recovery performance of the proposed scheme, we establish a quasi-isometric embedding with a high probability for mixed-precision quantization. The effectiveness of our proposed scheme is demonstrated through numerical results, highlighting successful reconstruction.

Phase-modulated continuous wave (PMCW) is a digital modulation waveform being investigated for modern automotive radar systems as a potential alternative to commonly used frequency-modulated continuous wave (FMCW) waveforms. In prior research, we introduced stepped-frequency PMCW (SF-PMCW), a variant of PMCW, which generates a synthetic bandwidth across multiple pulses, allowing lower sampling rates and more cost-effective analog-to-digital converters (ADCs). However, utilizing stepped-frequency waveforms introduces a quadratic phase shift, which causes dispersion in the range-Doppler map, thereby reducing the likelihood of target detection. This work proposes an approach aiming to compensate for the quadratic phase shift. This method decreases the effects of energy dispersion, thereby significantly enhancing target detection performance.

As automotive radars continue to proliferate, there is a continuous need for improved performance and several critical problems that need to be solved. All of this is driving research across industry and academia. This paper is an overview of research areas that are centered around signal processing. We discuss opportunities in the area of modulation schemes, interference avoidance, spatial resolution enhancement and application of deep learning. A rich list of references is provided. This paper should serve as a useful starting point for signal processing practitioners looking to work in the area of automotive radars.

Millimeter-wave (mmWave) radars are critical for autonomous vehicles’ perception tasks, offering reliable performance in adverse weather conditions. However, their application is often hindered by insufficient spatial resolution for detailed semantic scene interpretation. Traditional super-resolution methods derived from optical imaging fail to accommodate the unique properties of radar signals. Addressing this, our study redefines radar imaging super-resolution as a one-dimensional (1D) signal super-resolution spectra estimation problem, leveraging domain-specific insights to innovate data normalization and introduce a domain-informed signal-to-noise ratio (SNR)-guided loss function. Our custom deep learning network, tailored for automotive radar imaging, achieves substantial improvements in parameter efficiency, and inference speed while enhancing image quality and resolution. Comprehensive tests demonstrate that our SR-SPECNet establishes a new standard for high-resolution radar range-azimuth imaging, surpassing previous methods. Source code and new radar dataset will be made publicly available at https://github.com/ruxinzh/SR_DOA.

Single-snapshot signal processing in sparse linear arrays has become increasingly vital, particularly in dynamic environments like automotive radar systems, where only limited snapshots are available. These arrays are often utilized either to cut manufacturing costs or result from unintended antenna failures, leading to challenges such as high sidelobe levels and compromised accuracy in direction-of-arrival (DOA) estimation. Despite deep learning’s success in tasks such as DOA estimation, the need for extensive training data to increase target numbers or improve angular resolution poses significant challenges. In response, this paper presents a novel Siamese neural network (SNN) featuring a sparse augmentation layer, which enhances signal feature embedding and DOA estimation accuracy in sparse arrays. We demonstrate the enhanced DOA estimation performance of our approach through detailed feature analysis and performance evaluation. The code for this study is available at https://github.com/ruxinzh/SNNS_SLA.

Single-snapshot direction-of-arrival (DOA) estimation using sparse linear arrays (SLAs) has gained significant attention in the field of automotive MIMO radars. This is due to the dynamic nature of automotive settings, where multiple snapshots aren’t accessible, and the importance of minimizing hardware costs. Low-rank Hankel matrix completion has been proposed to interpolate the missing elements in SLAs. However, the solvers of matrix completion, such as iterative hard thresholding (IHT), heavily rely on expert knowledge of hyperparameter tuning and lack task-specificity. Besides, IHT involves truncated-singular value decomposition (t-SVD), which has a high computational cost in each iteration. In this paper, we propose an IHT-inspired neural network for single-snapshot DOA estimation with SLAs, termed IHT-Net. We utilize a recurrent neural network structure to parameterize the IHT algorithm. Additionally, we integrate shallow-layer autoencoders to replace t-SVD, reducing computational overhead while generating a novel optimizer through supervised learning. IHT-Net maintains strong interpretability as its network layer operations align with the iterations of the IHT algorithm. The learned optimizer exhibits fast convergence and higher accuracy in the full array signal reconstruction followed by single-snapshot DOA estimation. Numerical results validate the effectiveness of the proposed method.

This paper addresses the direction-of-arrival (DOA) estimation problem of mixed coherent and uncorrelated signals using a sparse rectangular array, where tensor reconstruction is employed to preserve the structure of multi-dimensional array signals. In the proposed approach, we first estimate the DOAs of uncorrelated signals using the subspace algorithm. After eliminating the contribution of uncorrelated signals from the covariance tensor, a structural tensor decorrelation process is introduced to decorrelate the resulting coherent covariance tensor. The canonical polyadic decomposition method is employed to the decorrelated covariance tensor to detect the coherent signals. The conditions of signal resolvability are analyzed.

We consider an integrated sensing-communication system operating in a dynamic environment, such as an autonomous vehicle scenario. We propose a novel, low-cost, low power consumption and low-computation approach for designing a beam that can simultaneously reach the radar target of interest and the desired communication destination. The transmitter is a uniform linear array, equipped with quantized double phase shifters, which enables a flexible beam design while using analog only processing. Only a small number of antennas are selected to transmit in each channel use, in order to save system power and reduce antenna coupling. We propose a deep reinforcement learning approach to adaptively adjust the double phase shifters and select the active antennas in order to optimize the transmit beamforming, through a transmission and feedback trail. The actor-critic network strategy together with the Wolpertinger policy is adopted to obtain the optimal solutions efficiently and effectively. Numerical results demonstrate the feasibility of the proposed method.

We propose a novel automotive radar imaging technique to provide high-resolution information in four dimensions, i.e., range, Doppler, azimuth, and elevation, by exploiting a joint sparsity design in frequency spectrum and array configurations. Random sparse step-frequency waveform is proposed to synthesize a large effective bandwidth and achieve high range resolution profiles. This concept is extended to multi-input multi-output (MIMO) radar by applying phase codes along the slow time to synthesize a two-dimensional (2D) sparse array with a high number of virtual array elements which enable high-resolution direction finding in both azimuth and elevation. The 2D sparse array acts as a sub-Nyquist sampler of the corresponding uniform rectangular array (URA), and the corresponding URA response is recovered by completing a low-rank block Hankel matrix. The proposed imaging radar provides point clouds with a resolution comparable to light detection and ranging (LiDAR) but with a much lower cost and is insensitive to weather conditions.

We consider an automotive radar using a sparse linear array (SLA) in the context of multi-input multi-output (MIMO) radar. The key problem in SLA is the selection of the locations of the array elements so that the peak sidelobe level of the virtual SLA beampattern is low. Prior approaches have focused on optimal sparse array design, or use of interpolation techniques for filling the holes in the synthesized SLA before applying digital beamforming for angle finding. In this paper, different from previous efforts, we use matrix completion to complete the corresponding virtual uniform linear array (ULA) before estimating the target angle. In particular, we show that for a small number of targets within the same range-Doppler cell, the Hankel matrix constructed by subarrays of the virtual ULA is low-rank, and thus under certain conditions, can be completed based on the SLA measurements. We derive the coherence properties of the Hankel matrix so that the matrix can be competed via nuclear norm minimization methods. We also demonstrate via examples the effect of various SLA topologies on the identifiability of the Hankel matrix.

The paper proposes a matrix completion based colocated MIMO radar (MIMO-MC) approach that employs transmit beamforming. The transmit antennas transmit correlated waveforms to illuminate certain directions. Each receive antenna performs sub-Nyquist sampling of the target returns at uniformly random times, and forwards the samples to a fusion center along with information on the sampling times. Based on the forwarded samples, the fusion center partially fills a matrix, recovers the Nyquist rate samples via matrix completion, and subsequently proceeds with target estimation via standard techniques. The performance of matrix completion depends on the matrix coherence. The paper derives the relations between transmit waveforms and matrix coherence. Specifically, it is shown that, for a rank-1 beamformer, the coherence is optimal, i.e., 1, if and only if the waveforms are unimodular. For a multi-rank beamformer, the coherence of the row space of the data matrix is optimal if the waveform power is constant across each snapshot. Simulation results show that the proposed scheme achieves high resolution with a significantly reduced number of samples.

We consider a colocated MIMO radar scenario, in which the receive antennas forward their measurements to a fusion center. Based on the received data, the fusion center formulates a matrix which is then used for target parameter estimation. When the receive antennas sample the target returns at Nyquist rate, and assuming that there are more receive antennas than targets, the data matrix at the fusion center is low-rank. When each receive antenna sends to the fusion center only a small number of samples, along with the sample index, the receive data matrix has missing elements, corresponding to the samples that were not forwarded. Under certain conditions, matrix completion techniques can be applied to recover the full receive data matrix, which can then be used in conjunction with array processing techniques, e.g., MUSIC, to obtain target information. Numerical results indicate that good target recovery can be achieved with occupancy of the receive data matrix as low as 50%.

Conventional distributed power control algorithms in cognitive radio networks are based on the assumption of perfect channel state information (CSI) which may lead to performance degradation in practical systems. In this paper, we investigate the robust distributed power control problem in cognitive radio networks by considering the uncertainty of channel gains. Our objective is to minimize the total power consumption of cognitive transmitters under both QoS constraint at each cognitive receiver and interference constraint at primary receiver. The uncertainty of channel gain is described using ellipsoid sets and the robust power control problem can be formulated as a semi-infinite programming (SIP) problem. It can be transformed to a second order cone programming (SOCP) problem by considering the worst cases of constraints. We apply the dual decomposition theory to solve the robust power control problem in a distributed way. To reduce the overhead of message passing among all cognitive users, an asynchronous iterative algorithm is then proposed and its convergence is also proved. Numerical results show that when there is uncertainty of channel gains, by using the proposed robust algorithms, both the primary user interference constraint and the target SINR requirement of each cognitive receiver can be guaranteed.