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Device-free passive wireless localization system with weighted transferable discriminative dimensionality reduction method
Telecommunication Systems ( IF 2.5 ) Pub Date : 2020-05-16 , DOI: 10.1007/s11235-020-00675-9
Xinping Rao , Zhi Li , Yanbo Yang , Yueqing Wang

Device-free passive wireless indoor localization has attracted great interest due to the widespread deployment of Wi-Fi devices and the rapid growth in demand for location-based services. In this paper, we propose a novel device-free passive wireless localization system with a weighted transferable discriminative dimensionality reduction method (termed TLLOC). It utilizes the channel state information (CSI) extracted from a single link to estimate the location of the target, neither requiring the target to wear any electronic equipment nor deploying a large number of APs and Monitor Devices. To cope with the problem of reduced localization accuracy caused by the unpredictable nature of CSI over time that ignored by most previous CSI-based localization works, a novel weighted transferable discriminative dimensionality reduction (termed WTR) method combining transfer learning and dimensionality reduction is proposed. The WTR method constructs a low-dimensional latent space, which can simultaneously improve the discrimination of training samples and narrow the distribution divergence between the training samples and the test samples, further enhancing the performance of our system. Experimental results are presented to confirm that TLLOC can effectively improve localization accuracy while saving a great amount of the calibration cost, compared with the other existing methods in a representative indoor environment.



中文翻译:

加权可转移判别降维方法的无设备无源无线定位系统

由于Wi-Fi设备的广泛部署以及对基于位置的服务的需求的快速增长,无设备的无源无线室内本地化引起了人们的极大兴趣。在本文中,我们提出了一种新型的无设备被动无线定位系统,该系统采用加权可转移判别降维方法(称为TLLOC)。它利用从单个链路中提取的信道状态信息(CSI)来估计目标的位置,既不需要目标佩戴任何电子设备,也不需要部署大量的AP和监视设备。为了解决由于CSI随时间变化的不可预测性而导致的本地化精度降低的问题,而大多数先前基于CSI的本地化工作都忽略了该问题,提出了一种结合转移学习和降维的加权可转移判别降维方法(简称WTR)。WTR方法构造了一个低维的潜在空间,可以同时改善训练样本的辨别力并缩小训练样本与测试样本之间的分布差异,从而进一步提高系统的性能。实验结果表明,与代表性室内环境中的其他现有方法相比,TLLOC可以有效提高定位精度,同时节省大量校准成本。可以同时改善训练样本的辨别力,缩小训练样本与测试样本之间的分布差异,从而进一步提高系统的性能。实验结果表明,与代表性室内环境中的其他现有方法相比,TLLOC可以有效提高定位精度,同时节省大量校准成本。可以同时改善训练样本的辨别力,缩小训练样本与测试样本之间的分布差异,从而进一步提高系统的性能。实验结果表明,与代表性室内环境中的其他现有方法相比,TLLOC可以有效提高定位精度,同时节省大量校准成本。

更新日期:2020-05-16
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