A Probabilistic Method-Based Smartphone GNSS Fault Detection and Exclusion System Utilizing PDR Step Length

Abstract

A smartphone equipped with a Global Navigation Satellite System (GNSS) module can generate positional information for location-based services. However, GNSS signals are susceptible to fragility, multipath (MP), and Non-Line-Of-Sight (NLOS) interference, which can lead to a degradation in the accuracy of GNSS positioning on smartphones. Due to limitations in the smartphone’s antenna, GNSS signal strength is typically lower. Moreover, in urban areas, where smartphones rely on GNSS, MP and NLOS signals are the primary factors impeding accurate positioning. In this paper, with the goal of enhancing both the accuracy and robustness of smartphone GNSS positioning, we propose two methods. Firstly, an optimized particle filter method employing a Krill Herd Algorithm (KHA) is suggested for the integration of GNSS and Pedestrian Dead Reckoning (PDR). Secondly, a probabilistic approach is presented to identify faulty GNSS measurements using step distance information obtained from the PDR. Experimental tests were conducted using smartphones to evaluate the performance of the proposed method. The results demonstrate that both the KHA and fault detection methods effectively enhance the performance of integrated PDR and GNSS.

An Effective GNSS/PDR Fusion Positioning Algorithm on Smartphones for Challenging Scenarios

“…In the outdoor scenarios, fusion positioning based on GNSS and PDR has also been widely studied and applied to smart terminals. Recently, Jiang et al [ 13 ] proposed an optimized particle filter method using the krill herd algorithm (KHA) for GNSS/PDR integration, significantly improving the integration positioning accuracy. Zhu et al [ 14 ] analyzed the error characteristics of the inertial measurement unit (IMU) of smartphones and designed an improved GNSS/PDR fusion positioning method.…”