Multipath Ghost Classification for MIMO Radar Using Deep Neural Networks

被引：0

作者：

Feng, Ruoyu ^{[1
,2
]}

De Greef, Eddy ^{[1
]}

Rykunov, Maxim ^{[1
]}

Sahli, Hichem ^{[1
,3
]}

Pollin, Sofie ^{[1
,2
]}

Bourdoux, Andre ^{[1
]}

机构：

[1] IMEC, Kapeldreef 75, B-3001 Leuven, Belgium

[2] Katholieke Univ Leuven, ESAT, Kasteelpk Arenberg 10, B-3001 Heverlee, Belgium

[3] Vrije Univ Brussel, ETRO, Pl Laan 2, B-1050 Brussels, Belgium

来源：

2022 IEEE RADAR CONFERENCE (RADARCONF'22) | 2022年

关键词：

MIMO radar; multipath; ghost classification; deep neural networks;

D O I：

10.1109/RADARCONF2248738.2022.9764274

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

Multipath is a significant challenge for indoor multiple-input-multiple-output (MIMO) radar applications. It generates the so-called 'ghosts' in the radar detection, which represent the objects that do not exist. Targets and ghosts are very similar, which makes them difficult to be recognized without prior knowledge of the environment geometry. In this work, a multipath model for the indoor scenario is analyzed for a frequency-modulated continuous-wave (FMCW) MIMO radar. Based on the multipath model, spatial signals from the MIMO virtual channels are fed to a deep neural network that is proposed to classify the multipath ghost, combined with a linear pattern recognition algorithm from our previous work. Simulation and experimental results demonstrate the performance of the proposed solution.

引用

页数：6

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