Smartphone-Based Pedestrian NLOS Positioning Based on Acoustics and IMU Parameter Estimation

This paper proposes an integrated positioning algorithm for mobile devices and achieves long-term and high-accuracy indoor pedestrian tracking under severe non-Line-of-Sight (NLOS) scenarios. The traditional fusion method for hand-held devices lacks zero-speed correction and cannot clear the accumulated error of the Pedestrian Dead Reckoning (PDR). Secondly, the PDR algorithm also requires user privacy data for high positioning accuracy. Hence, we propose a customized model with acoustic and PDR through self-updating parameters with two novel fusing strategies: Kalman Filter with Least-Square (KFLS) and Kalman Filter with Bayesian Parameter Estimation (KFBPE), which utilize numerical feedback and Bayesian distribution, respectively. Experiments with Huawei Mate 9 show that both methods above can effectively eliminate the outlier resulting from severe signal loss, regardless of hand-holding gestures, with no individual privacy data required. Extensive experimental results demonstrate that the proposed methods are more efficient for NLOS and perform much better than the baselines of traditional fusion frameworks like the standard Kalman Filter. KFBPE has a relatively smoother tracking result, which guarantees an average positioning accuracy of up to 25 cm under the circumstance of nearly thirty percent acoustic signal loss (or NLOS) at the same time.