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ResNet-Based Counting Algorithm for Moving Targets in Through-the-Wall Radar

Authors :
Yong Jia
Shengyi Chen
Guolong Cui
Yong Guo
Gang Wang
Ruiyuan Song
Xiaoling Zhong
Source :
IEEE Geoscience and Remote Sensing Letters. 18:1034-1038
Publication Year :
2021
Publisher :
Institute of Electrical and Electronics Engineers (IEEE), 2021.

Abstract

This letter mainly deals with the problem of counting moving human targets in an enclosed building space for through-the-wall radar. Specifically, a typical deep convolutional neural network, namely, residual neural network (ResNet), is designed to identify the line-like texture information associated with the target number from the blurred range-time images of a single-channel stepped-frequency continuous-wave (SFCW) radar. Experiments demonstrate that the ResNet-based counting algorithm achieves an accuracy of 91.54% for one to six human targets, and the accuracy rises to 97.12% when only counting one to three humans, even under conditions of wall penetration degradation, limited spatial resolution, heavy multipath clutters, and target-to-target occlusion. The achieved number of information of moving human targets not only contributes directly to the situation assessment behind the wall but also can act as the prior information to promote further target detection.

Details

ISSN :
15580571 and 1545598X
Volume :
18
Database :
OpenAIRE
Journal :
IEEE Geoscience and Remote Sensing Letters
Accession number :
edsair.doi...........2b9527a48c7cf5d3a741f2764b29554d
Full Text :
https://doi.org/10.1109/lgrs.2020.2990742