Automotive radar systems play a critical role in advanced driver assistance systems and autonomous vehicles by enabling reliable target detection and environment perception. Among these technologies, high-resolution four-dimensional (4D) multiple-input multiple-output (MIMO) radar has gained significant attention due to its ability to estimate range, velocity, azimuth, and elevation of targets simultaneously. This paper presents the design and performance analysis of a high-resolution 4D imaging automotive MIMO radar system using virtual array processing. The proposed radar system is modeled and simulated using MATLAB to evaluate key radar functionalities and signal processing techniques. The system employs frequency-modulated continuous-wave (FMCW) waveforms and Doppler Division Multiple Access (DDMA) to enable MIMO operation with a reduced number of physical antenna elements. A virtual antenna array is formed to improve angular resolution in both azimuth and elevation dimensions. Standard radar signal processing stages, including range–Doppler processing, constant false alarm rate (CFAR) detection, and beamforming, are implemented to generate a three-dimensional target point cloud with velocity information. Simulation results demonstrate that the proposed 4D MIMO radar system achieves high angular resolution and reliable target detection under automotive operating conditions. The proposed virtual array improvement with DDMA demonstrates competitive performance with a 0.5 m range resolution, ~ 2° azimuth and elevation beamwidths, and a 10 dB detection threshold. In congested situations, DDMA effectively resolves velocity ambiguities while enhancing target separability, elevation discrimination, and lowering false alarms. These findings provide useful advice for advanced automobile radar system design by demonstrating improved radar performance with preserved system efficiency. The findings demonstrate how DDMA-based FMCW MIMO radar systems can provide high-resolution sensing for widespread automobile deployment while preserving practical system complexity and cost effectiveness. Nevertheless, the evaluation provided relies on a simulation framework driven by a cost model and excludes comprehensive hardware prototyping or field measurements, potentially influencing the absolute performance assessments.
Vasa et al. (Sat,) studied this question.