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首页> 外文期刊>IEEE Geoscience and Remote Sensing Letters >ResNet-Based Counting Algorithm for Moving Targets in Through-the-Wall Radar
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ResNet-Based Counting Algorithm for Moving Targets in Through-the-Wall Radar

机译：基于RESEN的计数算法，用于移动墙壁雷达中的目标

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摘要

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.
机译：这封信主要涉及在围墙雷达的封闭式建筑空间中计算移动人体目标的问题。 具体地，典型的深度卷积神经网络，即剩余神经网络（Reset），旨在识别与从单声道阶梯式连续的模糊的范围图像相关联的线状纹理信息。 波（SFCW）雷达。 实验表明，基于Reset的计数算法对于一至六个人体目标的准确度为91.54％，并且当仅在墙壁渗透性降级，空间分辨率有限，空间分辨率有限，空间分辨率有限的条件下，精度上升至97.12％。 多径追踪和目标到目标闭塞。 实现人类目标的信息数量不仅直接贡献墙背后的情况，而且可以充当促进进一步目标检测的前提信息。

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