Millimeter-wave sensing using automotive radar imposes high requirements on the applied signal processing in order to obtain the necessary resolution for current imaging radar. High-resolution direction of arrival estimation is needed to achieve the desired spatial resolution, limited by the total antenna array aperture. This work gives an overview of the recent progress and work in the field of deep learning based direction of arrival estimation in the automotive radar context, i.e. using only a single measurement snapshot. Additionally, several deep learning models are compared and investigated with respect to their suitability for automotive angle estimation. The models are trained with model- and data-based approaches for data generation, including simulated scenarios as well as real measurement data from more than 400 automotive radar sensors. Finally, their performance is compared to several baseline angle estimation algorithms like the maximum-likelihood estimator. All results are discussed with respect to the estimation error, the resolution of closely spaced targets and the total estimation accuracy. The overall results demonstrate the viability and advantages of the proposed data generation methods, as well as super-resolution capabilities of several architectures.

Owing to the ubiquity of cellular communication signals, positioning with the fifth-generation (5G) signal has emerged as a promising solution in global navigation satellite system-denied areas. Unfortunately, although the widely employed antenna arrays in 5G remote radio units (RRUs) facilitate the measurement of the direction of arrival (DOA), DOA-based positioning performance is severely degraded by array errors. This article proposes an in situ calibration framework with a user terminal (UT) transmitting 5G reference signals at several known positions in the actual operating environment and the accessible RRUs estimating their array errors from these reference signals. Further, since sub-6 GHz small-cell RRUs deployed for indoor coverage generally have small-aperture antenna arrays, while 5G signals have plentiful bandwidth resources, this work segregates the multipath components via super-resolution delay estimation based on the maximum likelihood criteria. This differs significantly from existing in situ calibration works which resolve multipaths in the spatial domain. The superiority of the proposed method is first verified by numerical simulations. We then demonstrate via field test with commercial 5G equipment that, a reduction of 46.7% for $1{\text -}\sigma$ DOA estimation error can be achieved by in situ calibration using the proposed method.

Digitally modulated radar systems, such as phase-modulated continuous wave (PMCW), often struggle with high bandwidth demands for fine-range resolutions, posing challenges for cost-effective automotive applications. To address this issue, we propose a stepped-frequency PMCW (SF-PMCW) radar waveform in which the instantaneous bandwidth of a single pulse is extensively reduced while the range resolution is beyond the theoretical limit imposed by the instantaneous bandwidth. The proposed waveform spans a synthetic bandwidth across multiple pulses, achieving range estimates comparable to those typically achieved with higher instantaneous bandwidths. Simultaneously, the requirements for analog-to-digital converters (ADCs) are relaxed. Simulations have been performed to demonstrate the performance. The results indicate that the proposed SF-PMCW waveform with an instantaneous bandwidth of 100MHz can achieve range estimates as good as a PMCW waveform with an instantaneous bandwidth of 1GHz.

Commercial automotive radar systems for advanced driver assistance systems (ADASs) have relied on frequency-modulated continuous wave (FMCW) waveforms for years due to their low-cost hardware, simple signal processing, and established academic and industrial expertise. However, FMCW systems face several challenges, including limited unambiguous velocity, restricted multiplexing of transmit signals, and susceptibility to interference. This work introduces a unified automotive radar signal model and reviews the alternative modulation schemes such as phase-coded frequency-modulated continuous wave (PC-FMCW), phase-modulated continuous wave (PMCW), orthogonal frequency-division multiplexing (OFDM), orthogonal chirp division multiplexing (OCDM), and orthogonal time frequency space (OTFS). These schemes are assessed against key technological and economic criteria and compared with FMCW, highlighting their respective strengths and limitations.

This paper presents an exhaustive review of recent advancements in interference mitigation strategies for automotive radar systems, focusing on the intrinsic characteristics of interference across various radar modulation schemes. It categorizes these strategies into four main groups based on their inherent properties, facilitating a detailed comparative analysis. The primary objective is to enhance understanding in this crucial field and bridge the gap between existing research and comprehensive reviews. Additionally, the paper highlights the practical challenges of implementing these strategies, providing insightful implications for engineering and industry practices. Importantly, it emphasizes the critical role of regulatory agencies and the need for cross-disciplinary collaboration to tackle the challenges associated with interference mitigation effectively.