Sparse Learning via Iterative Minimization With Application to MIMO Radar Imaging

作者：Tan Xing^*; Roberts William; Li Jian; Stoica Petre

来源：IEEE Transactions on Signal Processing, 2011, 59(3): 1088-1101.

DOI：10.1109/TSP.2010.2096218

• Bayesian information criterion
• MIMO radar
• radar imaging
• sparse signal recovery
• sparse learning via iterative minimization

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 l(1)-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 (SLIM) follows an l(1)-norm constraint (for 0 < q <= 1), and can thus be used to provide more accurate estimates compared to the l(1)-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.

• 出版日期2011-3