Hybrid signal DOA estimation method under sparse Bayes learning framework

A sparse Bayesian and mixed-signal technology, applied to radio wave direction/deviation determination systems, direction finders using radio waves, instruments, etc., to achieve high direction finding accuracy, source number estimation, and good reliability Effect

Active Publication Date: 2017-12-05
HARBIN ENG UNIV
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Problems solved by technology

[0005] The purpose of the present invention is to propose a mixed-signal DOA estimation method under the sparse Bayesian learning framework, which can solve the problem that the e

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  • Hybrid signal DOA estimation method under sparse Bayes learning framework
  • Hybrid signal DOA estimation method under sparse Bayes learning framework
  • Hybrid signal DOA estimation method under sparse Bayes learning framework

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[0055] This article will be further described in detail below in conjunction with the drawings and specific embodiments:

[0056] 1. Obtain sparse signal sampling data;

[0057] Assuming that a total of K far-field narrowband signals are incident on a uniform linear array with an array element number of M, the array element spacing d=λ2. Divide the angular space into J sampling grids The number of grids J usually satisfies J>>M>>K. If Is the true incident direction θ to the target j The nearest sampling grid, then h j (t) = 0, otherwise h j (t)≈s jk (t) For j k =1,2,...,K and j =1,2,...,J are established. At this time, the data received by the antenna array is:

[0058]

[0059] among them, Represents the steering vector; H(t)=[h 1 (t),h 2 (t),…,h J (t)] T ; N(t) represents the noise vector. Since H(t) contains only K non-zero elements, H(t) is a sparse vector. For L snapshots, the array output is:

[0060]

[0061] Among them, X=[X(1),X(2),...,X(L)]; H=[H(1),H(2),...,H(L)]; ...

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Abstract

The invention discloses a hybrid signal DOA estimation method under the sparse Bayes learning framework and belongs to the radar signal processing technology field. The method comprises steps that firstly, a new sparse promotion prior called as a Gauss-index-chi square prior is proposed, a probability density function of the prior has a sharp spectrum peak and a heavy tail at a zero point, and sparse solution is facilitated; secondly, the prior is utilized to establish a third-order layered sparse Bayes model; thirdly, the mean value field variation Bayes theory is utilized to realize theoretical approximate posteriori distribution, alternative update iteration of each approximate variation distribution is carried out to minimize a KL distance, and estimates of model parameters are solved; lastly, a signal power spectrum function is constructed according to the estimates of the model parameters, and source number estimation and DOA estimation of a target radiation source can be acquired.

Description

Technical field [0001] The invention relates to a mixed signal DOA estimation method under a sparse Bayesian learning framework, and belongs to the technical field of radar signal processing. Background technique [0002] The direction of arrival (DOA) estimation is one of the important research directions in the field of array signal processing. The technology mainly processes the data received by the sensor array arranged in a certain way in space to estimate Parameters such as the number of sources, direction of incoming waves, and source frequency of target incident signals have broad application prospects in many fields such as radar, wireless communication, and navigation. In the actual application environment, the multipath propagation effect of the signal makes the signal received by the antenna array often no longer a single uncorrelated signal, but a mixed signal composed of uncorrelated signals and coherent signals, so the DOA estimation of mixed signals is studied It...

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Application Information

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IPC IPC(8): G01S3/14G01S7/41
CPCG01S3/14G01S7/41
Inventor 司伟建赵嫔姣曲志昱侯长波张春杰张朝柱乔玉龙王利伟
Owner HARBIN ENG UNIV
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