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A Meshless Sparse Spectral Estimation Method Based on Polynomial Root Finding

A polynomial root-finding and gridless technology, applied in the field of signal processing, can solve problems such as difficult selection of GFCS hyperparameters, and achieve the effect of avoiding DOA estimation errors

Active Publication Date: 2022-03-15
NORTHWESTERN POLYTECHNICAL UNIV
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  • Abstract
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  • Application Information

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Problems solved by technology

[0006] In order to avoid the deficiencies of the prior art, the present invention proposes a gridless sparse spectrum estimation method based on polynomial root-finding, which can achieve more accurate DOA estimation for static or slow-moving target signals in slow-changing environments, and at the same time Solve the problem of difficult selection of hyperparameters in GFCS, and solve the problem of gridless DOA estimation in the case of multiple snapshots

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  • A Meshless Sparse Spectral Estimation Method Based on Polynomial Root Finding
  • A Meshless Sparse Spectral Estimation Method Based on Polynomial Root Finding
  • A Meshless Sparse Spectral Estimation Method Based on Polynomial Root Finding

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Embodiment Construction

[0041] Now in conjunction with embodiment, accompanying drawing, the present invention will be further described:

[0042] The technical solution adopted by the present invention to solve its technical problems comprises the following steps:

[0043] 1) Establish the received signal covariance matrix model

[0044] The M-element uniform line array with the element spacing of half wavelength is used as the receiving array to receive narrowband signals. Each sensor on the uniform line array converts the received underwater acoustic signal into an electrical signal, and obtains a discrete time-domain signal through an amplification circuit and a data collector x i (n), 1≤n≤N, i=1,...,M. Divide the space [-90°, 90°] (where 90° is the end-fire direction) into Q grids, and the vector composed of the direction angles represented by each grid point is denoted as Θ, Θ=[θ 1 ,θ 2 ,...,θ Q ]. On this discrete grid, the received signal of the array can be expressed as x(n)=A(Θ)s(n)+e...

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Abstract

The invention relates to a gridless sparse spectrum estimation method based on polynomial root finding. By solving the covariance matrix of array received signals, all snapshot numbers are combined to achieve the positioning of the method when the target moves slowly or is still. The accuracy is higher than that of the single-shot method, and it can be applied to lower signal-to-noise ratio situations. Reexpress the covariance matrix on the continuous space and establish the DOA estimation problem of the continuous space based on the model, solve the problem by using semi-definite programming so that the DOA estimation can be transformed into a polynomial root-finding, realize the DOA estimation on the continuous space, and avoid DOA estimation error due to insufficient mesh division.

Description

technical field [0001] The invention belongs to the field of signal processing and the like, and relates to a gridless sparse spectrum estimation method based on polynomial root finding. By establishing the DOA estimation problem on a continuous space, the DOA estimation error caused by the mismatch between grid division and signal azimuth is avoided , to achieve high-precision positioning in the case of slow or static target signals. Background technique [0002] Array signal processing is widely used in radar, sonar and other fields, and target orientation (Direction of arrival, DOA) estimation is a major task of array signal processing. Conventional DOA estimation methods include minimum variance distortion response beamforming (MVDR) and multiple signal classification (MUSIC). These methods can achieve high resolution, but require more snapshots. high. The DOA estimation method of sparse signal processing is a DOA estimation method developed in the past ten years. This...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G01S3/00
CPCG01S3/00
Inventor 杨益新张亚豪
Owner NORTHWESTERN POLYTECHNICAL UNIV