An efficient algorithm and pipelined VLSI architecture for the maximum likelihood estimation of directions of arrival

An efficient algorithm for optimizing the maximum likelihood criterion of direction-of-arrival (DOA) problems is presented. The algorithm is based on two principal theorems: the first concerns the fast Gram-Schmidt orthogonalization of a Krylov subspace, and the second provides an alternating one-dimensional maximization procedure. A combiner-lattice filter structure that facilitates highly modular and concurrent VLSI implementation for the proposed algorithm has also been developed. By investigating this structure, an instructive physical insight of the ML criterion could be revealed as the operation of notch filtering. Simulation results that demonstrate the performance of the algorithm are included