Fast sparse Bayesian learning based direction-of-arrival estimation method

Abstract

The invention discloses a fast sparse Bayesian learning based direction-of-arrival estimation method and mainly solves the problems of heavy computation and large location estimation error in the prior art. The method includes the implementation steps: (1) adopting antenna receivers to form a uniform linear array; (2) sampling space signals and computing an array covariance matrix R; (3) vectorizing R to obtain a sparse model vector y; (4) dividing space domain grids, and constructing an over-complete base phi(theta) according to the structure of the sparse model vector y; (5) establishing a sparse equation according to the sparse representation relation of the sparse model vector and the over-complete base; (6) defining a hyper-parameter vector alpha, and adopting a fast sparse Bayesian learning algorithm to solve the sparse equation; (7) drawing a magnitude spectrogram according to an optimal estimation value of alpha to obtain a direction-of-arrival value. By the method, estimation accuracy of target reconnaissance and passive location under the conditions of low signal to noise ratio and small snapshot number is improved, computational complexity is lowed, and 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] Signal DOA estimation is an important branch in the field of array signal processing. It refers to the use of antenna arrays to sense and receive space signals, and then use modern signal processing methods to quickly and accurately estimate the direction of the signal source. In radar, Sonar, wireless communication and other fields have important application value. With the continuous advancement of science and technology, there are increasingly higher requirements for the accuracy and resolution of signal direction of arrival estimation. [0003] At present, super-resolution DOA estimation techniques mainly include subspace methods and methods based on sparse representation. Subspace methods such as multiple signal classi...

AI Technical Summary

Problems solved by technology

Among them, the greedy algorithm has a large drop in estimation performance in the case of low signal-to-noise ratio, which can no longer meet the engineering requirements; while the convex optimization method has a very slow operation speed, and in the case of low signal-to-noise ratio, the estimation accuracy is not ideal

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.

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