Adaptive Subspace Detection for Wideband Radar Using Sparsity in Sinc Basis

The scenario that the moving range spread target (RST) contains the complicated motion is assumed in this letter, which means that its motion includes different nonconstant elements. Based on sparse representation, a new coherent integration method is proposed to improve the detection performance of the moving RST in Gaussian noise. Here, the sinc basis is introduced to sparsely represent the high-range-resolution profile (HRRP). Basis pursuit denoising (BPDN) recovers the HRRPs from their noisy measurements; hence, aligning the range bins can be implemented at low signal-to-noise ratios via the entropy minimization of adjacent coefficient vectors of the sparse HRRPs. Then, phase compensation is achieved by the recursive multiple-scatterer algorithm (RMSA) in order to acquire the coherent integration gain. Using the sinc basis, the adaptive subspace detector (ASD) is adopted to realize RST detection. Finally, the experimental results on raw data demonstrate the effectiveness of the proposed method.