Direction-of-arrival estimation method based on sparse representation

Abstract

The invention discloses a direction-of-arrival estimation method based on sparse representation. The method mainly solves the problems that because a similar technology is large in calculation amount and low in angular resolution, the estimation speed of target reconnaissance and passive location is low and estimation errors in target reconnaissance and passive location are large. The method comprises the implementation steps: (1) acquiring output signals of an array and calculating a covariance matrix R of the signals, (2) constructing reconstructed sparse vectors through lower triangular elements of the covariance matrix R, (3) carrying out mesh generation on a spatial domain to construct a perfect base, (4) converting the direction-of-arrival estimation issue into the issue of solving a constrained optimization equation according to the sparse representation relation between the reconstructed sparse vectors and the perfect base, (5) solving the constrained optimization equation according to the convex optimization method to obtain the optimal estimation vectors, (6) drawing a magnitude spectrum according to the one-to-one correspondence of the optimal estimation vectors and spatial domain angles to obtain the direction-of-arrival value. By means of the method, the calculation speed of target reconnaissance and passive location is decreased, and the estimation errors in target reconnaissance and passive location are reduced. The method can be applied to target reconnaissance and passive location.

Description

technical field [0001] The invention belongs to the technical field of signal processing, in particular to a direction-of-arrival angle estimation method, which can be used for target reconnaissance and passive positioning. Background technique [0002] Direction of Arrival Angle of Arrival (DOA) estimation is to use antenna arrays in different positions in space to receive signals from multiple signal sources in different directions, and use modern signal processing methods to quickly and accurately obtain the direction of signal sources. field has important application value. Among the models constructed for this problem, the subspace-based model appears earlier and is widely used. Most of the previous DOA estimation methods are generated using this model. In recent years, the theory of compressed sensing proposed by Donoho et al. provides a new way of thinking for DOA estimation, resulting in a class of DOA estimation methods based on sparse representation models. [00...

AI Technical Summary

Problems solved by technology

However, this method has a large amount of calculation, and in the case of low signal-to-noise ratio, the angular resolution is not ideal

In practical applications, since both target reconnaissance and passive positioning need to be carried out on the basis of angle estimation, the use of the above methods will cause the problems of slow response speed and large estimation error of target reconnaissance and passive positioning

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