INTRI: Contour-Based Trilateration for Indoor Fingerprint-Based Localization

Due to its accuracy, trilateration has been widely deployed to locate smartphones outdoors. However, such approach cannot be easily applied indoors due to issues like non-line-of-sight measurement and complex multipath fading. Though fingerprinting overcomes these issues, its accuracy is often hampered by signal noise and the similarity metric comparing signal vectors. We propose INTRI, a novel, simple, accurate, and effective indoor localization framework combining strengths of trilateration and fingerprinting. Given a signal level received from an access point (AP) at target, INTRI first forms a contour given by reference points (RPs) with the same signal level, taking into account signal noise. The target is hence at the juncture of contours formed by all APs. We present selecting RPs for random signal by a width parameter determining the signal contour width (or spread). Then, an LP-based formulation finds the location following spirit of trilateration, which minimizes distance between target position and all contours. A novel particle filter leverages crowdsourced user inputs to adaptively estimate the width parameter. An online algorithm is further used to calibrate heterogeneous smartphones. Our extensive experiments in an airport, a shopping mall, and our campus show INTRI outperforms recent schemes with substantially lower error (often by more than 20 percent).