FREDI: Robust RSS-based ranging with multipath effect and radio interference

Radio Signal Strength (RSS) based ranging is attractive for mobile device localization due to low cost and easy deployment. In real environments, its accuracy is severely affected by the multipath effect and external radio interference. The well-studied fingerprinting approaches overcome these problems but introduce high overhead in dynamic environments. In this paper, we address these issues using a completely different approach. We propose a new ranging framework called Fredi that exploits the frequency diversity to overcome the multi-path effect solely based on RSS measurements. Specifically, we design a Discrete Fourier Transformation based algorithm and prove that it has the optimal solution under ideal cases. We further make the algorithm be adaptive and robust to address measurement errors and external radio interference, which are inevitable in practice. We implement Fredi on top of the USRP-2 platform and conduct extensive real experiments in three different indoor environments. Experimental results show the superiority performance compared to the traditional methods. The ranging errors are consistently less than 0.5m within 4m distance and 1m within 6m distance in dynamic environments much more accurate than existing solutions using online RSS measures. Other critical factors that influence the accuracy, such as the antenna polarization and Huygens Effect are also discussed and studied.