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Data-Driven Passive Localization With Non-Cooperative Radiation Sources via Mutually Inverse Networks
IEEE Communications Letters ( IF 3.7 ) Pub Date : 2020-01-24 , DOI: 10.1109/lcomm.2020.2968034
Hao Wu , Yongqiang Cheng , Hongqiang Wang

This letter proposes a passive localization method with non-cooperative radiation sources and distributed receivers. In this letter, a pair of mutually inverse networks is put forward. The inverse network aims to generate more labeled data, and the original network is trained with the generated data to learn the mapping from the observed data to the target location. Experimentally, the results show the proposed method can achieve better performance with one-sixteenth the amount of training data than the network in same structure. The proposed method has the potential in outdoor passive localization with communication base stations.

中文翻译:


通过互逆网络使用非合作辐射源进行数据驱动的被动定位



这封信提出了一种采用非合作辐射源和分布式接收器的无源定位方法。在这封信中,提出了一对互逆网络。逆网络旨在生成更多标记数据,并用生成的数据训练原始网络以学习从观察数据到目标位置的映射。实验结果表明,与相同结构的网络相比,该方法只需十六分之一的训练数据量即可获得更好的性能。所提出的方法在通信基站的室外无源定位中具有潜力。
更新日期:2020-01-24
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