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Micro-Doppler parameter estimation via multiple measurement vector model

The micro-Doppler (m-D) feature is regarded as a unique characteristic for target recognition. Sparse recovery based approaches for m-D parameter estimation using the single measurement vector (SMV) model have shown their effectiveness recently. However, SMV only fits for narrowband m-D signals, and accurate parameters can hardly be estimated using SMV in strong noise. The signals with the same sparse supports can be represented as multiple measurement vector (MMV). The m-D signal cannot be directly modeled as MMV, since it has different sparse patterns due to the rapid micro-motion. In this paper, a modified MMV-based m-D parameter estimation method is proposed for wideband m-D signals in strong noise. The micromotion period can be first extracted in the slow-time profile. As the structure of the m-D signals is the same in the specific slowtime unit of each micro-motion period, the MMV model of m-D signals can be established based on the same sparse pattern in the dictionary, which is built by the m-D signal's intrinsic structure. By solving the sparse solution of MMV, the m-D parameters are estimated based on the non-zero rows of the sparse solution. Simulation results show that the effectiveness of the proposed method outperforms the SMV-based one in strong noise.