Tan, Xing; Roberts, William; Li, Jian (Department of Electrical and Computer Engineering, University of Florida, USA)
Stoica, Petre (Department of Information Technology, Uppsala University, Sweden)
Multiple-input multiple-output (MIMO) radar can provide higher resolution, improved sensitivity, and increased parameter identifiability compared to phased-array radar schemes. When a scene of interest contains only a limited number of targets, sparse signal recovery algorithms, including many l1-norm based approaches, can be used to perform MIMO angle-range-Doppler imaging. Herein, we present a regularized minimization approach to sparse signal recovery. Sparse Learning via Iterative Minimization, or SLIM, follows an lq-norm constraint (for 0 < q ? 1), and can thus be used to provide sparser estimates, compared to the l1-norm based approaches, for MIMO radar imaging.