Magnetic-Map-Matching-Aided Pedestrian Navigation Using Outlier Mitigation Based on Multiple Sensors and Roughness Weighting

Abstract

This research proposes an algorithm that improves the position accuracy of indoor pedestrian dead reckoning, by compensating the position error with a magnetic field map-matching technique, using multiple magnetic sensors and an outlier mitigation technique based on roughness weighting factors. Since pedestrian dead reckoning using a zero velocity update (ZUPT) does not use position measurements but zero velocity measurements in a stance phase, the position error cannot be compensated, which results in the divergence of the position error. Therefore, more accurate pedestrian dead reckoning is achievable when the position measurements are used for position error compensation. Unfortunately, the position information cannot be easily obtained for indoor navigation, unlike in outdoor navigation cases. In this paper, we propose a method to determine the position based on the magnetic field map matching by using the importance sampling method and multiple magnetic sensors. The proposed method does not simply integrate multiple sensors but uses the normalization and roughness weighting method for outlier mitigation. To implement the indoor pedestrian navigation algorithm more accurately than in existing indoor pedestrian navigation, a 15th-order error model and an importance-sampling extended Kalman filter was utilized to correct the error of the map-matching-aided pedestrian dead reckoning (MAPDR). To verify the performance of the proposed indoor MAPDR algorithm, many experiments were conducted and compared with conventional pedestrian dead reckoning. The experimental results show that the proposed magnetic field MAPDR algorithm provides clear performance improvement in all indoor environments.

Probabilistic Map Matching for Robust Inertial Navigation Aiding

“…The simulation results suggest that, using a low noise field sensor, the PMHT-MM algorithm is able to use a batch state of small length to yield a robust aided inertial navigation performance without track divergence. • The proposed algorithm may be used for map matching with other types of sensor measurements, such as the gravity gradient tensor (Jekeli, 2006), magnetometer measurements (Kim et al, 2019), or terrain-based navigation (Nygren & Jansson, 2004), etc.…”

Probabilistic Map Matching for Robust Inertial Navigation Aiding

“…• The proposed algorithm may be used for map matching with other type of sensor measurements, such as the gravity gradient tensor (Jekeli, 2006), magnetometer measurements (Kim et al, 2019), or terrain-based navigation (Nygren & Jansson, 2004),etc.…”

An Enhanced Hidden Markov Model for Map-Matching in Pedestrian Navigation

“…The main objective of a map-matching algorithm is to map a sequence of observed GPS data to a road segment, providing more accurate and reliable location information for many ITS services such as navigation, map update/inference, object tracking, and traffic prediction [1][2][3][4][5][6][7][8][9][10][11][12]. The HMM is the foundation of map-matching methods as it is capable of handling noisy observations and complex road networks [13].…”

“…The conventional methods define the initial probabilities over states as a uniform distribution and the emission probability distribution based on the distance between the GPS measurements and the closest road segments. Despite wide acceptance in vehicular navigation, HMM-based map-matching methods have not received much attention in pedestrian navigation due to the unique challenges that remain to be overcome [1,2]. The localization error of GPSs in smartphones used by pedestrians is one of the critical factors that limit the large-scale deployment of pedestrian navigation applications.…”