Noncircularity-based generalized shift invariance for estimation of angular parameters of incoherently distributed sources

In this paper, a reduced-rank angular parameters estimation algorithm is proposed for incoherently distributed (ID) noncircular sources based on uniform linear array (ULA), which addresses the problems of central direction-of-arrival (DOA) and spread estimation. Firstly, noncircularity property signals utilized to establish an extended generalized manifold (GAM) model first-order Taylor series approximation. Then, DOAs source are obtained shift invariance principle. Next, spreads estimated from moments distribution. Compared with existing without exploiting information, one can achieve higher accuracy handle more sources. addition, it deal general scenario where different have distribution shapes. Furthermore, approximate stochastic Cramer-Rao bound (CRB) concerned problem derived. Simulation results provided demonstrate performance algorithm.