For Review O nly MIMO Radar Imaging

Through waveform diversity, multiple-input multiple-output (MIMO) radar can provide higher resolution, improved sensitivity, and increased parameter identifiability compared to more traditional phased-array radar schemes. Existing methods for target estimation, however, often fail to provide accurate MIMO angle-range-Doppler images when there are only a few data snapshots available. Sparse signal recovery algorithms, including many l1-norm based approaches, can offer improved estimation in that case. In this paper, 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 more accurate estimates compared to the l1-norm based approaches. We herein compare SLIM, through imaging examples and examination of computational complexity, to several well-known sparse methods, including the widely-used CoSaMP approach. We show that SLIM provides superior performance for sparse MIMO radar imaging applications at a low computational cost. Furthermore, we will show that the user parameter q can be automatically determined by incorporating the Bayesian information criterion. IEEE Transactions on Signal Processing Submitted in December, 2009 EDICS#: SAM-RADR 1This material is based on research sponsored in part by the U. S. Army Research Laboratory and the U. S. Army Research Office under contract/grant No. W911NF-07-1-0450, the National Science Foundation (NSF) under Grant No. ECCS-0729727, the Office of Naval Research under Grant No. N00014-09-1-0211, the Komen Breast Cancer Foundation under grant No. BCTR0707587, the SMART Fellowship Program, the Swedish Research Council (VR), and the European Research Council (ERC). The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for Governmental purposes notwithstanding any copyright notation thereon. 2Xing Tan is with the Department of Electrical and Computer Engineering, University of Florida, Gainesville, FL 32611-6130, USA. Phone: (352) 3925241; Fax: (352) 392-0044; Email: [email protected]. 3William Roberts is with the Department of Electrical and Computer Engineering, University of Florida, Gainesville, FL 32611-6130, USA. Phone: (352) 392-5241; Fax: (352) 392-0044; Email: [email protected]. 4Jian Li is with the Department of Electrical and Computer Engineering, University of Florida, Gainesville, FL 32611-6130, USA. Phone: (352) 392-2642; Fax: (352) 392-0044; Email: [email protected]. Please address all correspondence to Jian Li. 5Petre Stoica is with the Department of Information Technology, Uppsala University, Uppsala, Sweden. Phone: 46-18-471-7619; Fax: 46-18-511925; Email: [email protected].