Extended Kalman Filter for Real Time Indoor Localization by Fusing WiFi and Smartphone Inertial Sensors

Abstract

Indoor localization systems using WiFi received signal strength (RSS) or pedestrian dead reckoning (PDR) both have their limitations, such as the RSS fluctuation and the accumulative error of PDR. To exploit their complementary strengths, most existing approaches fuse both systems by a particle filter. However, the particle filter is unsuitable for real time localization on resource-limited smartphones, since it is rather time-consuming and computationally expensive. On the other hand, the light computation fusion approaches including Kalman filter and its variants are inapplicable, since an explicit RSS-location measurement equation and the related noise statistics are unavailable. This paper proposes a novel data fusion framework by using an extended Kalman filter (EKF) to integrate WiFi localization with PDR. To make EKF applicable, we develop a measurement model based on kernel density estimation, which enables accurate WiFi localization and adaptive measurement noise statistics estimation. For the PDR system, we design another EKF based on quaternions for heading estimation by fusing gyroscopes and accelerometers. Experimental results show that the proposed EKF based data fusion approach achieves significant localization accuracy improvement over using WiFi localization or PDR systems alone. Compared with a particle filter, the proposed approach achieves comparable localization accuracy, while it incurs much less computational complexity.

RFID Based Indoor Positioning System Using Event Filtering

“…There have been many studies on sensor based positioning systems in the indoor environment [19,20,25]. [19] proposed a novel data fusion framework by using an extended Kalman filter to integrate WiFi localization with pedestrian dead reckoning.…”

“…[19] proposed a novel data fusion framework by using an extended Kalman filter to integrate WiFi localization with pedestrian dead reckoning. [20] proposed a sensor fusion framework for combining WiFi, PDR and landmarks for indoor localization.…”

“…For indoor location based service, the LBS needs to provide the accurate locations of objects within a small area [4,21,22,25]. However, it is presently impossible to provide accurate location estimation via indoor LBS because of the inaccuracy of the locations that are provided based on GPS [7,18,19]. To overcome the latter problem, various sensor technologies such as Active Badge [13], RADAR [14], and Cricket [15], TELIAMADE [21], Smart-Condo [24], LEMON [28] have been proposed.…”

Pedestrian Dead Reckoning Based on Motion Mode Recognition Using a Smartphone

“…Handheld-PDR utilizes handheld mobile devices to obtain the locations and headings of pedestrians, which usually consists of three modules: step detection, step length estimation and heading determination. However, there are still some limitations in the existing techniques, as many localization approaches assume that the heading angle offset remains constant, the heading angle offset is the angle between the direction of smartphone and the direction of pedestrian [15,16,17]. The assumption can be satisfied when pedestrians hold smartphones on the front of the body or when pedestrians are making calls.…”

Robust Heading Estimation for Indoor Pedestrian Navigation Using Unconstrained Smartphones

“…Most existing heading estimation solutions deploy traditional attitude estimation based approaches [9,10]. The user heading is estimated by adding a fixed user heading offset to the estimated device forward heading.…”