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一种具有时空相关性的K分布海杂波的新型重构方法。

A Novel Reconstruction Method of K-Distributed Sea Clutter with Spatial-Temporal Correlation.

作者信息

Ding Mingyue, Li Yachao, Quan Yinghui, Guo Liang, Xing Mengdao

机构信息

National Laboratory of Radar Signal Processing, Xidian University, Xi'an 710071, China.

The School of Physics and Optoelectronic Engineering, Xidian University, Xi'an 710071, China.

出版信息

Sensors (Basel). 2020 Apr 22;20(8):2377. doi: 10.3390/s20082377.

DOI:10.3390/s20082377
PMID:32331380
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7219325/
Abstract

The reconstruction of sea clutter plays an important role in target detection and recognition in a maritime environment. Reproducing the temporal and spatial correlations of real data simultaneously is always a problem in the reconstruction of sea clutter due to the complex coupling between them. In this paper, the spatial-temporal correlated proportional method (STCPM), based on a compound model, is proposed to reconstruct K-distributed sea clutter with correlation characteristics obtained from the real data. The texture component with spatial-temporal correlation is generated by the proportional method and the speckle component with temporal correlation is generated by matrix transformation. Compared with previous methods, the biggest innovation of the STCPM is that it can more accurately generate K-distributed sea clutter with both temporal and spatial correlations. The comparison of the reconstructed and real data demonstrates that the method can reproduce the characteristics of real sea clutter well.

摘要

海杂波的重建在海洋环境中的目标检测与识别中起着重要作用。由于实际数据的时间和空间相关性之间存在复杂的耦合关系，在海杂波重建中同时再现它们的时空相关性一直是一个难题。本文提出了一种基于复合模型的时空相关比例法（STCPM），用于重建具有从实际数据中获得的相关特性的K分布海杂波。具有时空相关性的纹理分量通过比例法生成，具有时间相关性的斑点分量通过矩阵变换生成。与先前的方法相比，STCPM最大的创新之处在于它能够更准确地生成具有时间和空间相关性的K分布海杂波。重建数据与实际数据的比较表明，该方法能够很好地再现真实海杂波的特性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3fd1/7219325/c4580998a429/sensors-20-02377-g015.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3fd1/7219325/16f6dbd6fa84/sensors-20-02377-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3fd1/7219325/e911963b596a/sensors-20-02377-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3fd1/7219325/f79d76e528ab/sensors-20-02377-g012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3fd1/7219325/00c977aad9f8/sensors-20-02377-g013.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3fd1/7219325/7f3c4b1e86b4/sensors-20-02377-g014.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3fd1/7219325/c4580998a429/sensors-20-02377-g015.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3fd1/7219325/eeff7fe80a82/sensors-20-02377-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3fd1/7219325/f9702ffb6ce5/sensors-20-02377-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3fd1/7219325/c5ee7dc1e3f2/sensors-20-02377-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3fd1/7219325/300d12e4dee5/sensors-20-02377-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3fd1/7219325/60c3aca1f269/sensors-20-02377-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3fd1/7219325/533360dfaa98/sensors-20-02377-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3fd1/7219325/16f6dbd6fa84/sensors-20-02377-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3fd1/7219325/e911963b596a/sensors-20-02377-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3fd1/7219325/f79d76e528ab/sensors-20-02377-g012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3fd1/7219325/00c977aad9f8/sensors-20-02377-g013.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3fd1/7219325/7f3c4b1e86b4/sensors-20-02377-g014.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3fd1/7219325/c4580998a429/sensors-20-02377-g015.jpg

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