Joint velocity and angle estimation for single-cycle TDM MIMO automotive radar

• Published in: Digital signal processing 2024-10, Vol.153, p.104617, Article 104617
• Main Authors: Geng, Xueyin, Wang, Jun, Yang, Bin, Sun, Jinping, Gini, Fulvio, Greco, Maria Sabrina
• Format: Article
• Language: English
• Subjects: Angle-Doppler coupling; IAA; Joint velocity and angle estimation; TDM MIMO radar
• ISSN: 1051-2004; 1095-4333
• DOI: 10.1016/j.dsp.2024.104617

Time-division multiplexing (TDM) multiple-input multiple-output (MIMO) radar systems have gained popularity in automotive applications, thanks to their high-resolution and cost-effective properties. However, when the relative motion between the target and the radar is significant and possibly fast time-varying, TDM MIMO systems suffer for reduced maximum unambiguous velocity and angle-Doppler coupling. In this work, a novel off-grid estimation method for TDM MIMO systems is developed that operates on a single cycle and is able to accurately estimate unambiguously the instantaneous velocity and the target direction of arrival (DOA). We model the angle-Doppler coupling through a space-time steering vector. Then, an iterative adaptive approach (IAA)-based method is exploited to reconstruct the 2D velocity-angle spectrum. Additionally, we account for off-grid errors when the targets do not lie on the search grids. A coarse-to-fine strategy is proposed to alleviate the computational burden, where a maximum likelihood (ML) estimator iteratively corrects the off-grid velocity and DOA estimates of each target. Simulated and experimental data are processed to demonstrate the effectiveness of the proposed method. •A generalized time-varying signal model based on coding matrix Φ is used to describe the TDM MIMO radar angle-Doppler coupling.•An off-grid and low-computational joint velocity and angle estimation method based iterative adaptive approach (IAA) and coarse-to-fine principle for single-cycle TDM MIMO radar.•Simulated and experimental results demonstrate that the proposed method can estimate off-grid angles and ambiguous velocities, with high resolution and fast convergence capabilities.