Multi-way Compressive Sensing Based 2D DOA Estimation Algorithm for Monostatic MIMO Radar with Arbitrary Arrays

This paper addresses the problem of two-dimensional (2D) direction of arrival (DOA) estimation for monostatic multiple-input multiple-output (MIMO) radar equipped with arbitrary arrays manifold. A low-complexity multi-way compressive sensing (MCS) 2D DOA estimation algorithm is derived in this paper. To ease the computational burden, the received data are first arranged into a trilinear model, and then three random compression matrixes are individually applied to reduce each tensor to a much smaller tensor. Finally, the 2D DOA estimation problem then links to the low-dimensional trilinear model. The proposed algorithm has estimation performance close to that of the trilinear decomposition algorithm while it is more appealing form the perspective of storage capacity. Compared with other estimation algorithms, such as multiple signal classification, our MCS algorithm requires neither eigenvalue decomposition of the received signal covariance matrix nor spectral peak searching. It also doesn’t require array interpolation of the array geometry, therefore the proposed algorithm has much less computational load, which indicates the proposed algorithm will have wide application in the future for Massive MIMO systems.