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通过二维反卷积与矩阵补全实现高分辨率转台雷达成像。

High Resolution Turntable Radar Imaging via Two Dimensional Deconvolution with Matrix Completion.

作者信息

Lu Xinfei, Xia Jie, Yin Zhiping, Chen Weidong

机构信息

Key Laboratory of Electromagnetic Space Information, Chinese Academy of Sciences, University of Science and Technology of China, Hefei 230027, China.

Academy of Photoelectric Technology, Hefei University of Technology, Hefei 230009, China.

出版信息

Sensors (Basel). 2017 Mar 8;17(3):542. doi: 10.3390/s17030542.

DOI:10.3390/s17030542
PMID:28282904
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC5375828/
Abstract

Resolution is the bottleneck for the application of radar imaging, which is limited by the bandwidth for the range dimension and synthetic aperture for the cross-range dimension. The demand for high azimuth resolution inevitably results in a large amount of cross-range samplings, which always need a large number of transmit-receive channels or a long observation time. Compressive sensing (CS)-based methods could be used to reduce the samples, but suffer from the difficulty of designing the measurement matrix, and they are not robust enough in practical application. In this paper, based on the two-dimensional (2D) convolution model of the echo after matched filter (MF), we propose a novel 2D deconvolution algorithm for turntable radar to improve the radar imaging resolution. Additionally, in order to reduce the cross-range samples, we introduce a new matrix completion (MC) algorithm based on the hyperbolic tangent constraint to improve the performance of MC with undersampled data. Besides, we present a new way of echo matrix reconstruction for the situation that only partial cross-range data are observed and some columns of the echo matrix are missing. The new matrix has a better low rank property and needs just one operation of MC for all of the missing elements compared to the existing ways. Numerical simulations and experiments are carried out to demonstrate the effectiveness of the proposed method.

摘要

分辨率是雷达成像应用的瓶颈，它受到距离维度的带宽和横向距离维度的合成孔径的限制。对高方位分辨率的需求不可避免地导致大量的横向距离采样，这总是需要大量的发射-接收通道或较长的观测时间。基于压缩感知（CS）的方法可用于减少采样，但存在测量矩阵设计困难的问题，并且在实际应用中不够稳健。本文基于匹配滤波器（MF）后回波的二维（2D）卷积模型，提出了一种用于转台雷达的新型二维反卷积算法，以提高雷达成像分辨率。此外，为了减少横向距离采样，我们引入了一种基于双曲正切约束的新矩阵补全（MC）算法，以提高欠采样数据的MC性能。此外，对于仅观测到部分横向距离数据且回波矩阵某些列缺失的情况，我们提出了一种新的回波矩阵重建方法。与现有方法相比，新矩阵具有更好的低秩特性，并且对于所有缺失元素仅需一次MC操作。进行了数值模拟和实验以证明所提方法的有效性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b5bc/5375828/04135512773f/sensors-17-00542-g017.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b5bc/5375828/91c5c73814a0/sensors-17-00542-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b5bc/5375828/a3bcd848dabb/sensors-17-00542-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b5bc/5375828/fb2c6be2f222/sensors-17-00542-g012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b5bc/5375828/298445690503/sensors-17-00542-g013.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b5bc/5375828/0f2a2aed1821/sensors-17-00542-g016.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b5bc/5375828/04135512773f/sensors-17-00542-g017.jpg

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