Robust Passive Location in Zero-Calibrated Environment Using Smoothed Ordinal Constraints

Passive locating by capturing radio signal strength (RSS) from mobile phone's WiFi probing messages in zerocalibrated environments is a challenging problem, because of 1) the lack of accurate RSS-distance model; 2) measurement noise, and 3) the dynamic environment factors affecting the measurements. This paper investigates the noise features of RSS signals by practical experiments, and investigates whether the feasible region bounded by trustworthy ordinal relationships (TOR) among RSS measurements can help to improve the location accuracy. A constrained non-linear optimization model is proposed to apply the TOR constraints for target localization. A series of methods for smoothed feasible region mergence over time are investigated, including 1) Ordinal constraint fusion in one time-slot (OS); 2) Ordinal constraint intersection of multiple time-slots (IM); 3) Ordinal constraint fusion of multiple time-slots using expansion and kernel (EK). The remarkable accuracy and robustness improvements benefited from using TOR constraints were demonstrated by both simulations and practical experiments compared with state-of-the-art passive locating methods.