Contour-based Trilateration for Indoor Fingerprinting Localization
Abstract: Trilateration has been widely and successfully employed to locate outdoor mobile devices due to its accuracy. However, it cannot be directly applied for indoor localization due to issues such as non-line-of-sight measurement and multipath fading. Though fingerprinting overcomes these issues, its accuracy is often hampered by signal noise and the choice of similarity metric between signal vectors. We propose INTRI, a novel, simple and effective indoor localization technique combining the strengths of trilateration and fingerprinting.For a signal level received from an access point (AP) by the target, INTRI first forms a contour consisting of all the reference points (RPs) of the same signal level for that AP, taking into account the signal noise. The target is hence at the juncture of all the contours. With an optimization formulation following the spirit of trilateration, it then finds the target location by minimizing the distance between the position and all the contours. INTRI further uses an online algorithm based on signal correlation to efficiently calibrate heterogeneous mobile devices to achieve higher accuracy. We have implemented INTRI, and our extensive simulation and experiments in an international airport, a shopping mall and our university campus show that it outperforms recent schemes with much lower location error (often by more than 20%).
Loading