An Enhanced Smartphone Indoor Positioning Scheme with Outlier Removal Using Machine Learning

Zhenbing Zhang,Lei Wang,Sheng Yang,Xiaodong Gong,Xingyu Zheng,Guangyi Guo,Gege Huang,Jingbin Liu

doi:10.3390/rs13061106

Abstract

In smartphone indoor positioning, owing to the strong complementarity between pedestrian dead reckoning (PDR) and WiFi, a hybrid fusion scheme of them is drawing more and more attention. However, the outlier of WiFi will easily degrade the performance of the scheme, to remove them, many researches have been proposed such as: improving the WiFi individually or enhancing the scheme. Nevertheless, due to the inherent received signal strength (RSS) variation, there still exist some unremoved outliers. To solve this problem, this paper proposes the first outlier detection and removal strategy with the aid of Machine Learning (ML), so called WiFi-AGNES (Agglomerative Nesting), based on the extracted positioning characteristics of WiFi when the pedestrian is static. Then, the paper proposes the second outlier detection and removal strategy, so called WiFi-Chain, based on the extracted positioning characteristics of WiFi, PDR, and their complementary characteristics when the pedestrian is walking. Finally, a hybrid fusion scheme is proposed, which integrates the two proposed strategies, WiFi, PDR with an inertial-navigation-system-based (INS-based) attitude heading reference system (AHRS) via Extended Kalman Filter (EKF), and an Unscented Kalman Filter (UKF). The experiment results show that the two proposed strategies are effective and robust. With WiFi-AGNES, the minimum percentage of the maximum error (MaxE) is reduced by 66.5%; with WiFi-Chain, the MaxE of WiFi is less than 4.3 m; further the proposed scheme achieves the best performance, where the root mean square error (RMSE) is 1.43 m. Moreover, since characteristics are universal, the proposed scheme integrated the two characteristic-based strategies also possesses strong robustness.

Highlights

In recent years, location-based services (LBS) have become increasingly important due to their potential applications such as parcel or vehicle tracking, parking service, emergency responders, social networking, and mobile commerce [1]
We reasonably assume that the motion state of the pedestrian in smartphone indoor positioning comprises static and walking, based on the extracted positioning characteristics of WiFi when the pedestrian is static, we proposed the first outlier detection and removal strategy using Machine Learning (ML) named WiFi-AGNES
We proposed a hybrid fusion scheme which integrates the two proposed strategies, fingerprinting-based WiFi, Pedestrian Dead Reckoning (PDR) with an inertial-navigation-system-based (INS-based) attitude heading reference system (AHRS) via Extended Kalman Filter (EKF) for the azimuth estimation of PDR and an Unscented Kalman Filter (UKF) for the final fusion

Summary

Introduction

Location-based services (LBS) have become increasingly important due to their potential applications such as parcel or vehicle tracking, parking service, emergency responders, social networking, and mobile commerce [1]. 2021, 13, 1106 each technology has its advantages and disadvantages, the hybrid fusion scheme has become the mainstream research direction. Among these schemes, owing to the strong complementarity between PDR and WiFi, the fusion of them has gained plenty of attention [50,51,52,53,54,55,56], and becomes the research content of this paper

Methods

Results

Conclusion