Direction of Arrival Estimation Method Based on Spatial Smooth Covariance Matrix Sparse Representation

Abstract

The invention discloses a direction-of-arrival estimation method based on sparse representation of a spatial smoothing covariance matrix. The method mainly solves the problems that the calculation amount is large, the performance of processing coherent signal sources is poor and consequently errors in passive location estimation are large in the prior art. The method comprises the implementation steps: (1) forming a uniform linear array by antenna receivers, (2) the spatial smoothing covariance matrix output by the array is calculated according to the spatial smoothing technology, (3) vectorizing the spatial smoothing covariance matrix to obtain sparse model vectors, (4) carrying out mesh generation on a spatial domain to construct a perfect base, (5) establishing a constrained optimization equation based on the sparse representation relation between the sparse model vectors and the perfect base, (6) solving the constrained optimization equation according to the convex optimization method to obtain an optimal estimation value, and (7) drawing a magnitude spectrum according to the optimal estimation value to obtain the value of direction of arrival. By means of the method, the calculation speed of passive direction finding is increased, and the performance of estimating the coherent signal sources at a low signal-to-noise ratio is improved. The method can be applied to target reconnaissance and passive localization.

Description

technical field [0001] The invention belongs to the technical field of signal processing, in particular to a direction-of-arrival estimation method based on a uniform linear array, 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 at different positions in space to receive signals from multiple signal sources in different directions, and use modern signal processing methods to quickly and accurately estimate the direction of the signal source. It has important application value in other fields. In the research on this problem, subspace-based models such as multiple signal classification (MUSIC) appeared earlier and were widely used, and most subsequent DOA estimation algorithms were generated using this model. In recent years, the compressed sensing theory proposed by Donoho et al. provides a new way of thinking for DOA estimation problems, result...

AI Technical Summary

Problems solved by technology

In recent years, the L1_SRACV algorithm based on the sparse representation of the array covariance vector, the joint sparse approximation JLZA algorithm, etc. have appeared, but these algorithms have a large amount of calculation, and in the case of low signal-to-noise ratio, the angular resolution is not ideal, and the anti-coherent signal poor performance

However, in practical applications, both target reconnaissance and passive positioning need to be carried out on the basis of angle estimation. The defects in the above algorithms will cause the shortcomings of slow response speed and large estimation errors in target reconnaissance and passive positioning.

Images