Maxlifd: Joint Maximum Likelihood Localization Fusing Fingerprints and Mutual Distances

Fusing mutual distance information with fingerprints can substantially improve indoor localization accuracy. Such distance information may be spatial (e.g., measurement among users or from installed beaconing devices) or temporal (e.g., via dead-reckoning). Previous approaches on distance-fusion often require deterministic distance measurement, consider fingerprints and distances separately, or are narrowly applicable to some specific sensing technology or scenario. Given the fact that fingerprint and distance measurements are intrinsically random, we propose Maxlifd, an accurate indoor localization framework fusing fingerprints and distances of arbitrary distributions via joint maximum likelihood. Maxlifd is a generic statistical/probabilistic framework applicable to a wide range of sensors (peer-assisted, INS, iBeacon, etc.) and fingerprints (Wi-Fi, RFID, etc.). It achieves low localization errors by a novel optimization formulation jointly considering mutual distances and fingerprint signals. Using generic probabilistic formulation, we further derive the lower bound on localization error for comprehensive performance analysis. We have implemented Maxlifd, and conducted extensive simulation and experimental trials in an international airport and our university campus. Our results show that Maxlifd achieves significantly lower errors than other state-of-the-art schemes (often by more than 30 percent). We experimentally verify that its performance does not depend sensitively on the exact knowledge of the underlying distributions beyond simple Gaussian.