The problem of high-resolution direction of arrival (DOA) estimation based on a limited amount of snapshots in automotive multiple-input and multiple-output (MIMO) radar has been studied. The number of snapshots is restricted to min-imize target spread/migration in range and/or Doppler domains. A computationally efficient approach for side-looking arrays is developed, which combines the generation of motion-enhanced snapshots and MIMO technology, thus exploiting the movement of the vehicle and the spatial diversity of the transmit and receive antennas. Due to motion, a larger virtual aperture is obtained and the angular resolution is boosted, achieving the separation of targets that the traditional MIMO approach cannot discriminate, as well as better results than with other single snapshot DOA estimation techniques. Algorithm performance has been studied in simulations, and possible limitations have been discussed. In addition, the method has been verified experimentally with point-like and extended targets, and good agreement between simulations and experimental results has been observed.

The ambiguity problem of targets in Doppler beam sharpening with forward-looking radar is considered. While Doppler beam sharpening is proposed earlier to improve the angular resolution of the radar while keeping the antenna aperture size limited, such a solution suffers from ambiguities in the case of targets positioned symmetrically with respect to the platform movement. To address this problem, an approach named ’Unambiguous Doppler based forward-looking multiple-input multiple-output radar beam sharpening scan’ (UDFMBSC) is proposed, based on the combination of multiple-input multiple-output (MIMO) processing and Doppler beam sharpening. The performance of the proposed method is compared to existing approaches using simulated data with point-like and extended targets. The method is successfully verified using experimental data.

Sidelobe suppression is a basic but important task for synthetic aperture radar (SAR) images, since the existing sidelobes can reduce the image quality and complicate image interpretation. The main task of sidelobe suppression is to suppress the sidelobes effectively while maintaining high image resolution and the mainlobe's energy. However, the most effective method, robust spatially variant apodization (RSVA), still faces the problem of energy loss on cluttered targets caused by phase factors. This letter proposes an SAR sidelobe suppression method based on the convolutional neural network (CNN) to compensate for the energy loss in RSVA. The experiments on the SAR images show better performance on both sidelobe suppression evaluation metrics and signal-to-noise ratio (SNR) preservation compared with conventional methods.

The problem of 3D high resolution imaging in automotive multiple-input and multiple-output (MIMO) forward-looking radar has been studied. The coupling between the azimuth and elevation angle estimation and the ambiguity problem of the forward-looking Doppler beam sharpening are addressed jointly in the proposed novel algorithm, named ‘3D robust unambiguous Doppler beam sharpening with adaptive threshold’ (3DRUDAT). The performance of the algorithm has been studied in simulations and with an experimental dataset, using the image contrast and the F1-score of the voxelisation as proposed metrics to evaluate the 3D imaging results. Furthermore, the theoretical analysis of the improvement of the algorithm has been discussed with respect to more conventional approaches.

The problem of estimating the 3D ego-motion velocity using multi-channel FMCW radar sensors has been studied. For the first time, the problem of ego-motion estimation is treated using radar raw signals. A robust algorithm using multi-channel FMCW radar sensors to instantly determine the complete 3D motion state of the ego-vehicle (i.e., translational speed and rotational speed) is proposed. The angle information of targets is extracted, and then their phase information from different times instances is used to determine vehicle ego-motion through an optimization process. Any pre-processing steps, such as clustering or clutter suppression, are not required. The performance of the algorithm is compared with the state-of-the-art algorithms based on real-world data, and superior performance has been demonstrated. The algorithm proposed can be easily integrated into radar signal processing pipelines for other tasks relevant to autonomous driving.

The problem of 3D high-resolution imaging in automotive multiple-input multiple-output (MIMO) side-looking radar using a 1D array is considered. The concept of motion-enhanced snapshots is introduced to generate larger apertures in the azimuth dimension. For the first time, 3D imaging capabilities can be achieved with high angular resolution using a 1D MIMO antenna array, which can alleviate the requirement for large radar systems in autonomous vehicles. The robustness to variations in the vehicle’s movement trajectory is also considered and addressed with relevant compensations in the steering vector. The available degrees of freedom, as well as the Signal Noise Ratio (SNR), are shown to increase with the proposed method compared to conventional imaging approaches. The performance of the algorithm has been studied in simulations, and validated with experimental data collected in a realistic driving scenario.