Wide angle radar imaging under low SNR via sparsity enhanced non-negative matrix factorization

Narrow angle approximation and isotropic assumption adopted in regular radar imaging are violated in wide angle radar imaging scenario. Therefore, conventional Fourier based methods are not directly applicable, and full aperture algorithms perform poor under low SNR. This paper proposes an imaging scheme based on Sparsity Enhanced Non-negative Matrix Factorization (SENMF). The full aperture is firstly divided into several overlapping subapertures, to which Polar Format Algorithm (PFA) is then applied to obtain subimages at different aspects. Finally, NMF with sparsity regularization is exploited to iteratively composite the full aperture image, which demonstrates enhanced target feature and improved SNR. The results of Backhoe data processing verify the validity of the novel approach.