Two-Stage Sparse Bayesian Learning Algorithm for Multi-Target Localization in MIMO-FMCW Radar

As an effective approach for parameter estimation in radar systems, Sparse Bayesian Learning (SBL) has attracted significant attention due to its superior reconstruction performance in the field of direction-of-arrival (DOA) estimation for multiple targets. However, when applied to multi-target scenarios for joint range-angle estimation, traditional SBL methods face challenges in computational complexity and estimation accuracy that have not been well addressed. In this paper, we propose a novel two-stage SBL algorithm for multitarget localization in MIMO-FMCW radar systems. The proposed algorithm first performs coarse searching on sparse grids to detect potential targets, and then employs an iterative refinement process to achieve precise parameter estimation. This two-stage strategy not only improves localization accuracy but also reduces computational time. Monte Carlo simulation results demonstrate that our algorithm outperforms existing methods in terms of both estimation accuracy and computational efficiency.