Method for sparse DOA (Direction of Arrival) estimation under mutual coupling of array elements

Abstract

The invention relates to direction of arrival estimation for source signals, provides an algorithm for DOA (Direction of Arrival) estimation under a mutual coupling condition of unknown array elements. Under the premise of a sparse reconstruction theory, the algorithm makes full use of all information received by an array, and the angle estimation precision can be improved under strong mutual coupling of the array. The technical scheme adopted by the invention is that the method for sparse DOA estimation under mutual coupling of array elements comprises the steps of 1, calculating a covariance matrix R for a received signal X^(t); 2, performing singular value decomposition on the covariance matrix R; 3, calculating a signal subspace RS for receiving data according to a signal unitary space; 4, building a sparse signal complete dictionary AJ according to parameterization J of a steering vector; 5, building a convex programming function for sparse signal reconstruction according to a norm l1; 6, solving the convex programming function and performing spectrum peak search; and 7, further improving the estimation precision by using a principle of grid refinement. The method is mainly applied to direction of arrival estimation for signals.

Description

technical field [0001] The invention relates to the parameterization of the steering vector by using the inherent structural characteristics of the mutual coupling matrix under the assumption that the signal source is sparse and the uniform array has unknown mutual coupling information of the array elements without losing the received information of the array elements. On this basis, the principle of sparse reconstruction is used to estimate the direction-of-arrival (DOA) of the source signal. Background technique [0002] In spatial spectrum estimation, the earliest super-resolution angle estimation methods such as multiple signal classification (MUSIC) and rotation invariance (ESPRIT) algorithm belong to the algorithm of feature structure. These methods and their derivatives are based on the assumption that the flow pattern of the array is known precisely, and thus perform well. However, in practical engineering applications, the array antennas will interact with each oth...

AI Technical Summary

Problems solved by technology

Literature [4] applied the sparse signal reconstruction theory to the DOA estimation under the mutual coupling of unknown array elements. Theoretical derivation and experimental simulation proved its effectiveness, but the DOA estimation still needs the participation of auxiliary array elements.

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