A Training-Free Multipath Enhancement (TFME-RTI) Method for Device-Free Multi-Target Localization

Many intelligent perception systems are progressively approaching people's daily lives. Device-free localization (DFL), as a technology that can achieve positioning without the target being equipped with any equipment, has broad development prospects. RFID-based radio tomographic imaging (RTI) method has attracted much attention because of its low cost, easy deployment, and strong real-time performance. However, RTI, as a real-time imaging method that uses the line-of-sight path information and analyzes the received signal strength behavior, cannot effectively resist multipath interference. As the number of targets increases, the positioning performance using RTI method will be hit. In response to this problem, this paper proves the feasibility of applying static reflection multipath to device-free localization, and proposes a new training-free multipath enhancement (TFME-RTI) method using known spatial information. By analyzing the static multipath superposition model and target interference characteristics, a transformation model is established to take advantage of the multipath effect to enhance the positioning performance. Experiments demonstrate that the TFME-RTI method has better positioning performance and robustness in two typical indoor scenarios.

Automated summary

This paper introduces a new device-free localization method called TFME-RTI, which enhances the positioning performance by leveraging static reflection multipath. Experiments demonstrate that this method has improved positioning performance and robustness in two typical indoor scenarios.