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Mimo Radar DoA Estimation Method Based on Sampling Data Matrix Reconstruction

A technology of sampling data and DOA, which is applied in the field of MIMO radar DOA estimation, can solve the problems of missing target data, performance degradation and failure of DOA estimation algorithm, etc., and achieve the effect of improving DOA estimation accuracy, high real-time performance, and low processing complexity

Active Publication Date: 2022-05-20
NANJING UNIV OF INFORMATION SCI & TECH
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Problems solved by technology

[0005] When there is an array element failure in the MIMO radar array, a large number of entire rows of target data are missing in the virtual array sampling data matrix, resulting in the performance degradation or even failure of the existing DOA estimation algorithm

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  • Mimo Radar DoA Estimation Method Based on Sampling Data Matrix Reconstruction

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Embodiment Construction

[0064] The sampling data matrix of the MIMO radar virtual array not only has low rank but also has sparse characteristics. The scheme of the present invention combines low rank and sparse priors, not only to mine the correlation of elements between rows or columns of the matrix, but also to By making full use of the correlation of elements in a row or in a column, it is possible to restore the missing elements in the entire row in the MIMO radar sampling data matrix under the failure of the array element. By recovering the missing data of the failed array element, the DOA estimation performance of the MIMO radar under the array element failure can be improved.

[0065] The technical solutions of the present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0066] Such as figure 1 As shown, the MIMO radar DOA estimation method based on sampling data matrix reconstruction includes the following steps: ...

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Abstract

The invention relates to a MIMO radar DOA estimation method based on sampling data matrix reconstruction, which divides the failure of the array element of the MIMO radar into the failure of redundant virtual array elements and the failure of non-redundant virtual array elements. When the redundant virtual array element fails, the data of the normal working redundant virtual array element at the same position in space is averaged to fill the missing data of the failed array element, so as to reduce the influence of the array element failure on the estimation of the target DOA, and the algorithm processing is complicated. Low degree and high real-time performance. When the non-redundant virtual array element fails, the low-rank and sparse priors of the sampled data matrix of the MIMO radar virtual array can be used jointly, which can not only mine the correlation between rows or columns of the matrix, but also make full use of the intra-row or intra-column correlations. Correlation, high-precision reconstruction of the missing elements in the entire row of the data matrix after dimensionality reduction and filling, effectively improving the DOA estimation accuracy of the MIMO radar when the array element fails.

Description

technical field [0001] The invention belongs to the field of MIMO radar DOA estimation, and in particular relates to a MIMO radar DOA estimation method based on sampling data matrix reconstruction. Background technique [0002] The multiple-input multiple-output (Multiple-Input Multiple-Output, MIMO) technology has brought a new breakthrough to the radar system performance. Compared with traditional radar, MIMO radar has potential advantages in target resolution and parameter estimation, low interception and clutter suppression. Direction of Arrival (DOA) estimation is an important part of MIMO radar target parameter estimation. There are mainly DOA estimation methods such as subspace and sparse representation. In practical applications, due to the long-term aging of components and the influence of harsh environments, the antenna array elements will be physically damaged. Since the failed array elements cannot transmit and receive signals normally, there are a large number...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G01S7/41G01S3/14
CPCG01S7/41G01S3/14
Inventor 陈金立张程李家强朱艳萍
Owner NANJING UNIV OF INFORMATION SCI & TECH
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