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

机译:通过二维反卷积和矩阵完成实现高分辨率转台雷达成像

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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.
机译:分辨率是雷达成像应用的瓶颈,受限于范围尺寸的带宽和跨范围尺寸的合成孔径。对高方位角分辨率的需求不可避免地导致大量的跨范围采样,这总是需要大量的收发通道或较长的观察时间。基于压缩感测(CS)的方法可用于减少样本,但存在设计测量矩阵的困难,并且在实际应用中不够鲁棒。本文基于匹配滤波器后回波的二维(2D)卷积模型,提出了一种新颖的转盘雷达二维反卷积算法,以提高雷达成像分辨率。另外,为了减少跨范围样本,我们引入了一种基于双曲正切约束的矩阵完成(MC)新算法,以提高欠采样数据的MC性能。此外,针对仅观察到部分跨范围数据且缺少回波矩阵的某些列的情况,我们提出了一种回波矩阵重构的新方法。与现有方法相比,新矩阵具有更好的低秩属性,并且对于所有缺少的元素只需要执行一次MC操作。数值模拟和实验进行了证明该方法的有效性。

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