Moving target imaging for MIMO radar using CS-SLIM

Multiple-input Multiple-output (MIMO) radar can achieve superior performance through waveform diversity over conventional phased-array radar systems. Many methods were proposed to provide accurate MIMO angle-range-Doppler images of moving targets. Sparse Learning via Iterative Minimization (SLIM) can provide more accurate estimates in that case. However, when using the Bayesian Information Criterion (BIC) to determine the user parameter automatically for a less sparse scene, SLIM has high complexity. In this paper, we propose an improved approach as Sparse Learning via Iterative Minimization based on Compressive Sampling (CS-SLIM) for targets imaging. CS-SLIM can provide targets imaging as almost accurate as but with a lower computational burden than SLIM. The measurement matrix and the computational complexity for CS-SLIM are discussed. Mean Squared Errors (MSEs) at various measurement samples are provided to demonstrate the performance of the proposed approach in sparse scenes