Abstract:

Compressive sensing (CS)theory provides a new approach for inverse synthetic aperture radar(ISAR) to realize high-resolution imaging under very limited number of pulses. However, due to the noise sensitivity of CS, the quality of ISAR images suffers from noise contamination. In addition, in noise circumstance, it is hard to obtain accurate noise parameters estimation in the case of few pulses. This further exacerbates ISAR image contamination. To deal with this problem, a scattering region weighting compressive sensing ISAR imaging method is proposed. With the target region information, the weighting coefficients are determined for the basis function in the redundant dictionary. Then the CS reconstruction algorithm is modified with the target information weighting to suppress noise speckles. To improve the noise level estimation precision, the sub-sequence matrix is established from return samples,and then the matrix perturbation theory is performed to estimate the noise parameters. Experimental results indicate that the proposed method can effectively reduce noise and improve the image quality in the case of low signal-noise ratio and very limited number of pulses case.