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基于张量补全的随机时分复用 MIMO 雷达处理。

Random Time Division Multiplexing Based MIMO Radar Processing with Tensor Completion Approach.

机构信息

School of Information, North China University of Technology, Beijing 100144, China.

School of Information Science and Technology, Tsinghua University, Beijing 100084, China.

出版信息

Sensors (Basel). 2023 May 15;23(10):4756. doi: 10.3390/s23104756.

DOI:10.3390/s23104756
PMID:37430669
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10224449/
Abstract

Automotive radar pursues low cost and high performance, and especially hopes to improve the angular resolution under the condition of a limited number of multiple-input-multiple-output (MIMO) radar channels. Conventional time division multiplexing (TDM) MIMO technology has a limited ability to improve the angular resolution without increasing the number of channels. In this paper, a random time division multiplexing MIMO radar is proposed. First, the non-uniform linear array (NULA) and random time division transmission mechanism are combined in the MIMO system, and then a three-order sparse receiving tensor of a range-virtual aperture-pulse sequence is obtained during echo receiving. Next, this sparse three-order receiving tensor is recovered by using tensor completion technology. Finally, the range, velocity and angle measurements are completed for the recovered three-order receiving tensor signals. The effectiveness of this method is verified via simulations.

摘要

汽车雷达追求低成本和高性能，尤其希望在有限数量的多输入多输出（MIMO）雷达通道条件下提高角分辨率。传统的时分复用（TDM）MIMO 技术在不增加通道数量的情况下，提高角分辨率的能力有限。本文提出了一种随机时分复用 MIMO 雷达。首先，在 MIMO 系统中结合非均匀线阵（NULA）和随机时分传输机制，然后在回波接收过程中获得一个距离-虚拟孔径-脉冲序列的三阶稀疏接收张量。接下来，利用张量补全技术恢复这个稀疏的三阶接收张量。最后，对恢复的三阶接收张量信号完成距离、速度和角度测量。通过仿真验证了该方法的有效性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/362a/10224449/209317ca4686/sensors-23-04756-g014.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/362a/10224449/d9cb25273893/sensors-23-04756-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/362a/10224449/0094399f76f9/sensors-23-04756-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/362a/10224449/8c64e06b343c/sensors-23-04756-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/362a/10224449/8f0d475533c8/sensors-23-04756-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/362a/10224449/adf65b640962/sensors-23-04756-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/362a/10224449/d67780e6197f/sensors-23-04756-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/362a/10224449/b0ff4807c6e9/sensors-23-04756-g012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/362a/10224449/14822887ea35/sensors-23-04756-g013a.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/362a/10224449/209317ca4686/sensors-23-04756-g014.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/362a/10224449/6abac565e962/sensors-23-04756-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/362a/10224449/86c5ce74fe53/sensors-23-04756-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/362a/10224449/dbde319e37c4/sensors-23-04756-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/362a/10224449/822f295b2528/sensors-23-04756-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/362a/10224449/297c9f515ff7/sensors-23-04756-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/362a/10224449/d9cb25273893/sensors-23-04756-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/362a/10224449/0094399f76f9/sensors-23-04756-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/362a/10224449/8c64e06b343c/sensors-23-04756-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/362a/10224449/8f0d475533c8/sensors-23-04756-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/362a/10224449/adf65b640962/sensors-23-04756-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/362a/10224449/d67780e6197f/sensors-23-04756-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/362a/10224449/b0ff4807c6e9/sensors-23-04756-g012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/362a/10224449/14822887ea35/sensors-23-04756-g013a.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/362a/10224449/209317ca4686/sensors-23-04756-g014.jpg

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