Joint angular-frequency distribution estimation via spatial-temporal sparse sampling and low-rank matrix recovery

Abstract

This paper is committed to the joint angular-frequency distribution (JAFD) estimation for incoherently distributed wideband (IDW) sources by using spatial-temporal sparse sampling (STSS). The STSS increases dramatically the degrees of freedom (DOFs) of the measure system, and can solve the ambiguity problem between angular and frequency parameters. Then, it is observed that the discrete representation of the JAFD gives rise to a helpful low-rank matrix. To estimate the JAFD for IDW sources, a rank minimization problem is further formulated as the modified Schatten-p norm minimization (MSpNM) problem, which can be resolved efficiently by the accelerated iterative singular value thresholding algorithm (AISVTA). Compared with traditional methods based on spatial and temporal Nyquist sampling (STNS), the proposed method based on STSS reduces the sampling rate requirement on hardware and provides better JAFD estimation precision. Experiment simulations indicate its effectiveness and confirm the superior performance of the proposed method based on STSS.