Sparse array DOA estimation method based on PD-ALM algorithm

A sparse array, DOA technology, applied in the field of sparse array DOA estimation, can solve the problem of not getting the expected effect, achieve the effect of accurate incoming wave direction, good performance, and reduce mutual coupling effect

Active Publication Date: 2020-08-21
NANJING UNIV OF SCI & TECH
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Problems solved by technology

However, this method is suitable for large matrices with small ranks, and the expected effect cannot be obtained when applied to small sparse antenna arrays

Method used

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  • Sparse array DOA estimation method based on PD-ALM algorithm
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  • Sparse array DOA estimation method based on PD-ALM algorithm

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Effect test

Embodiment 1

[0072] The number of array elements of the uniform linear array is set to 16, 25, 36, 49, and 64, respectively, and the number of randomly closed array elements is 0.3 of the total number. At the same time, set the number of snapshots to 100, the signal-to-noise ratio to 10, and the interference source to be signals from two different directions.

[0073] Figure 4 In order to use the rank minimization-based matrix filling method of the present invention—the PD-ALM algorithm and the ALM algorithm based on nuclear norm minimization and DOA estimation under sparse arrays and full data under different numbers of array elements Root mean square error comparison. from Figure 4 It can be seen that the more the number of array elements in the array, the more effective information contained in the array receiving matrix, the smaller the root mean square error of DOA estimation, and the higher the performance of spatial spectrum estimation. However, when the number of arrays is sma...

Embodiment 2

[0075] Set the interference sources as signals from 1 direction, 2 different directions, and 3 different directions. At the same time, the number of array elements of the uniform line array is set to 20, and 8 array elements are randomly turned off. Set the number of snapshots to 100 and the signal-to-noise ratio to 10.

[0076] Figure 5 In the case of different numbers of interference sources, the matrix filling method based on rank minimization of the present invention-PD-ALM algorithm and the ALM algorithm based on nuclear norm minimization and DOA under sparse array and full data are adopted Estimated root mean square error comparison. from Figure 5 It can be seen that the more the number of interference sources, the greater the root mean square error of DOA estimation, and the lower the performance of spatial spectrum estimation. However, the performance of DOA estimation using the method of the present invention has always been significantly better than that of DOA...

Embodiment 3

[0078] Set the number of array elements of the uniform line array to 20, and set the number of randomly closed array elements to be 0.1, 0.2, 0.3, 0.4, 0.5, and 0.6 of the total number of array elements. At the same time, set the number of snapshots to 100, the signal-to-noise ratio to 10, and the interference source to be signals from two different directions.

[0079] Image 6 For arrays with different sparse ratios, using the rank minimization-based matrix filling method of the present invention——PD-ALM algorithm and the ALM algorithm based on nuclear norm minimization and DOA estimation under sparse arrays and full data Root mean square error comparison. from Image 6 It can be seen that the more closed array elements, the less effective information contained in the array receiving matrix, the greater the root mean square error of DOA estimation, and the lower the performance of spatial spectrum estimation. However, the performance of DOA estimation using the method of ...

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Abstract

The invention discloses a sparse array DOA estimation method based on a PD-ALM algorithm. The method comprises the following steps: rearranging sampling data obtained by each snapshot of a sparse array into a toeplitz matrix xT; constructing a matrix filling model based on rank minimization, and filling the matrix xT by using the PD-ALM algorithm to obtain a full matrix x 'T, wherein the first rowof data of the matrix x' T being the sampling data after the single snapshot completion; forming a data matrix X' by all the snapshot complemented sampling data; and finally, performing DOA estimation on the data matrix X' by using a DOA estimation algorithm. According to the PD-ALM algorithm provided by the invention, a penalty decomposition method is adopted to directly solve the rank minimization problem; when the method is applied to the sparse array, the received data matrix of the full array can be well recovered under the conditions that the number of array elements is relatively small, the number of interference sources is large and the array is sparse, so that the wave direction is estimated more accurately, and the direction finding performance of the sparse array is improved.

Description

technical field [0001] The invention belongs to radar signal processing technology, in particular to a sparse array DOA estimation method based on PD-ALM algorithm. Background technique [0002] In recent years, with the continuous development of compressed sensing and sparse reconstruction theory in the field of radar signal processing, DOA estimation methods based on compressed sensing and sparse reconstruction have been extensively studied. The matrix filling theory is derived from the compressed sensing theory, which can fill the matrix with missing data to obtain a complete matrix. [0003] Matrix filling mainly studies when only part of the data is observed in the matrix or some data in the matrix is ​​missing, using the correlation of known matrix elements to fill in the missing data. Mathematically, matrix filling can be described as an affine rank minimization problem. Due to the non-smooth and non-convex characteristics of the rank function, this type of problem i...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01S3/14
CPCG01S3/14
Inventor 芮义斌高进盈谢仁宏李鹏高媛李雨航杨恺文季宇豪
Owner NANJING UNIV OF SCI & TECH
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