In this paper, bias and CRB analysis for both proposed line-of-sight (LoS) and conventional radio-signal-strength (RSS) based positioning methods are derived. In contrast to the conventional lognormal based analysis, the derived error models take into account the environmental multipath fading effect. The proposed LoS-based scheme improves positioning accuracy by eliminating the effect of non-range dependent portion of powers from the received signal. It suppresses non-line-of-sight (NLoS) disturbance to the positioning result through estimating the LoS component from fading signal. Hence, it is robust to environmental fading disturbance. The derived error models are validated by simulation. Both analytical model and simulation results show that the accuracy and robustness of the proposed LoS-based positioning are superior to that of the conventional RSS-based methods.

In this paper, a cost efficient fusion scheme, Ubiquitous Tracking with Motion and Location Sensor (UTMLS), is proposed for the accurate localization and tracking in mixed GPS-friendly, GPS-challenging, and GPS-denied scenario. The proposed drift-reduction method in UTMLS addresses the cumulating error issue in the indoor tracking with the consumer grade motion sensor. The proposed hypothesis test method in UTMLS improves the tracking sensor fusion precision by detecting distorted GPS reports and intelligently switching between GPS and inertial sensor based schemes. The proposed scheme is instantiated and implemented on an Android smartphone platform. Experiments have been conducted to evaluate and validate the accuracy. Experimental results show that (1) the proposed drift-reduction method effectively suppresses the non-GPS tracking error accumulation due to the integration of acceleration noise with time (2) UTMLS realizes robust indoor/outdoor seamless tracking, preventing GPS fault estimates introduced tracking error in the conventional Kalman filtering process.