Nested array sparse representation direction-of-arrival estimation method based on maximum likelihood
A direction of arrival estimation and sparse representation technology, applied in the field of radar, can solve the problem of mismatch
Pending Publication Date: 2021-09-10
XIAN UNIV OF TECH
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[0006] Aiming at the problem of model mismatch in sparse representation DOA estimation methods, the existing methods are mainly divided into two categories, one is the multiple reconstruction method, and the calculation amount of the algorithm increases exponentially with the increase of the number of grids, and the other One is the Bayesian estimation method, although the problem of dictionary matrix grid mismatch is reduced, but there is still a mismatch
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[0067] The present invention will be described in detail below in conjunction with the accompanying drawings and specific embodiments.
[0068] The present invention is based on the maximum likelihood nested matrix sparse representation DOA estimation method, the flow chart is as follows figure 1 As shown, the specific steps are as follows:
[0069] Step 1. Calculate the echo signal of the receiving array according to the direction of arrival from the target to the receiving array of the nested array radar system and the array structure of the nested array.
[0070] The echo signal y(t) of the receiving array in step 1 is calculated as follows:
[0071] y(t)=A(θ)s(t)+n(t),
[0072] Among them, s(t)=[s 1 (t),s 2 (t),...,s K (t)] T Represents the signal vector, [·] T For the transposition operation, K represents the number of targets, and n(t) represents the channel noise vector, which is assumed to obey the complex Gaussian distribution, that is Represents a complex ...
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The invention discloses a nested array sparse representation direction-of-arrival estimation method based on maximum likelihood. The method comprises the following steps: firstly calculating an echo signal of a receiving array according to the direction of arrival from a target to a nested array and an array structure of the nested array, and then calculating a covariance matrix of the echo signal; according to a sparse representation theory, dividing the whole airspace in an angle dimension to obtain an angle set; vectorizing the covariance matrix, and carrying out sparse expansion on the angle set to obtain a nested array direction-of-arrival estimation sparse model; constructing a block diagonal matrix, and eliminating noise items in the sparse model to obtain a denoised nested array direction of arrival estimation sparse model; and calculating a noise whitening matrix in combination with the covariance matrix, calculating a noise-whitened data model in combination with the denoised nested array direction-of-arrival estimation sparse model, calculating grid maximum likelihood estimation, and finally obtaining a direction-of-arrival maximum likelihood estimation value. The DOA estimation performance of the nested array under the conditions of low signal-to-noise ratio and few snapshots is realized.
Description
technical field [0001] The invention belongs to the technical field of radar, and in particular relates to a method for estimating a direction of arrival based on a maximum likelihood-based sparse representation of nested arrays. Background technique [0002] Direction of arrival estimation technology is an indispensable technical means to realize target positioning, and the orientation information of the target in the airspace can be obtained through the estimation result of direction of arrival. Traditional typical methods include multiple signal classification method and rotation invariant subspace method. These two methods break through the Rayleigh limit and can achieve super-resolution of objects, but their performance is severely degraded in environments with low signal-to-noise ratio or few snapshots. [0003] The maximum likelihood estimation assumes that the signal source is a stochastic process with a known distribution, and uses the known sample result informatio...
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IPC IPC(8): G01S3/14G01S7/41
CPCG01S3/143G01S7/41
Inventor 赵永红辛菁李余兴张春丽吴思杰
Owner XIAN UNIV OF TECH