Transferring Compressive-Sensing-Based Device-Free Localization Across Target Diversity

Ju Wang, Xiaojiang Chen, Dingyi Fang, Chase Qishi Wu, Zhe Yang, Tianzhang Xing

Research output: Contribution to journalArticlepeer-review

65 Scopus citations


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.

Original languageEnglish (US)
Article number6909045
Pages (from-to)2397-2409
Number of pages13
JournalIEEE Transactions on Industrial Electronics
Issue number4
StatePublished - Apr 2015
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Electrical and Electronic Engineering


  • Transferring
  • compressive sensing
  • device-free localization
  • target diversity


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