Estimation method of quasi-stationary broadband array signal direction of arrival based on block sparse Bayesian learning

Abstract

The present invention discloses an estimation method of a quasi-stationary broadband array signal direction of arrival (DOA) based on block sparse Bayesian learning. An intra-frame correlation and aninterframe independence of a quasi-stationary broadband signal frequency spectrum are employed to set a corresponding a block sparse prior distribution model for signals, and a block sparse Bayesian model is employed to perform estimation of sparse signals, so that an estimation result with higher precision is obtained. Array receiving signals are subjected to appropriate framing processing, eachframe of the signal is subjected to Fourier transform, and each block sparse Bayesian model for each signal is established in a frequency domain; under an assumption of each frame of the signal is independent, information of all the frames is combined to establish a total Bayesian model, and hyper-parameter vectors are employed to control a sparsity of all the frames of signals to be reconstructed; and finally, the expectation maximization algorithm (EM) is employed to obtain an iterative update formula of the hyper-parameter vectors. The estimation method of a quasi-stationary broadband arraysignal direction of arrival based on block sparse Bayesian learning fully utilizes a short-time stability feature of quasi-stationary broadband array signals to establish a block sparse model, and therefore a higher DOA estimation precision can be obtained.

Description

technical field [0001] The invention belongs to the field of array signal processing, in particular to a method for estimating the direction of arrival of a quasi-stationary broadband array signal based on block sparse Bayesian learning. Background technique [0002] Direction of arrival estimation is an important research direction of array signal processing. With the wide application of broadband signals, how to effectively realize high-precision and high-resolution estimation of the direction of arrival of broadband signals has become a current research hotspot. In real life, the DOA estimation of quasi-stationary broadband array signals has a wide range of applications, such as the localization of sound sources by microphone arrays and the localization of video signals by air station systems. In the existing wideband array signal DOA estimation methods, subspace methods such as the two-side correlation transformation (Two-side Correlation Transformation, TCT) algorithm n...

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Problems solved by technology

In the existing wideband array signal DOA estimation methods, subspace methods such as the two-side correlation transformation (Two-side Correlation Transformation, TCT) algorithm need to accurately estimate the source orientation, and the algorithm performance is not accurate when the estimation is inaccurate. will drop sharply; sparse reconstruction methods such as Distributed Compressive Sensing-Simultaneous Orthogonal Matching Pursuit (DCS-SOMP) algorithm have low computational complexity, but their reconstruction performance is poor. , a series of algorithms that extend the L1-SVD (Singular Value Decomposition) method to wideband signal processing Because of the use of singular value decomposition, the number of sources needs to be known, and the performance is not ideal in the case of low signal-to-noise ratio

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