Transferring Compressive-Sensing-Based Device-Free Localization Across Target Diversity
Transferring Compressive-Sensing-Based Device-Free Localization Across Target Diversity
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DOI：
10.1109/tie.2014.2360140
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发表时间：
2015-04-01
影响因子：
7.7
通讯作者：
Xing, Tianzhang
中科院分区：
文献类型：
--
作者：
Wang, Ju;Chen, Xiaojiang;Xing, Tianzhang
无设备定位（DFL）在野生动物种群和迁徙跟踪等许多应用中发挥着重要作用。目前大多数DFL系统利用接收到的失真信号强度（RSS）变化来定位目标。然而，它们假设RSS变化测量的固定分布，尽管它们被不同类型的目标所扭曲。如果建模目标和测试目标属于不同的类别，则不可避免地会导致定位失败。本文提出了tlcs -一种基于传递压缩感知的DFL方法，该方法采用严格设计的传递函数将不同类别目标的扭曲RSS变化传递到潜在特征空间中，在潜在特征空间中，不同类别目标的扭曲RSS变化测量值的分布是统一的。这种方法的一个好处是，相同的传输传感矩阵可以被不同类别的目标共享，从而大大减少了人类的努力。实验结果证明了TLCS的有效性。
Device-free localization (DFL) plays an important role in many applications, such as wildlife population and migration tracking. Most of current DFL systems leverage the distorted received signal strength (RSS) changes to localize the target(s). However, they assume a fixed distribution of the RSS change measurements, although they are distorted by different types of targets. It inevitably causes the localization to fail if the targets for modeling and testing belong to different categories. This paper presents TLCS-a transferring compressive sensing based DFL approach-which employs a rigorously designed transferring function to transfer the distorted RSS changes across different categories of targets into a latent feature space, where the distributions of the distorted RSS change measurements from different categories of targets are unified. A benefit of this approach is that the same transferred sensing matrix can be shared by different categories of targets, leading to a substantial reduction in the human efforts. The results of experiments illustrate the efficacy of the TLCS.