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


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 amendment measurements, although they are distorted by completely 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—that employs a rigorously designed transferring operate to transfer the distorted RSS changes across totally 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 identical transferred sensing matrix can be shared by completely different classes of targets, leading to a substantial reduction within the human efforts. The results of experiments illustrate the efficacy of the TLCS.

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