Advanced Integration of WiFi and Inertial Navigation Systems for Indoor Mobile Positioning
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Advanced Integration of WiFi and Inertial Navigation Systems for Indoor Mobile Positioning ´ eric ´ Evennou and Franc¸ois Marx Fred Division R&D, TECH/IDEA, France Telecom, 38243 Meylan, France Received 23 June 2005; Revised 23 January 2006; Accepted 29 January 2006 This paper presents an aided dead-reckoning navigation structure and signal processing algorithms for self localization of an autonomous mobile device by fusing pedestrian dead reckoning and WiFi signal strength measurements. WiFi and inertial navigation systems (INS) are used for positioning and attitude determination in a wide range of applications. Over the last few years, a number of low-cost inertial sensors have become available. Although they exhibit large errors, WiFi measurements can be used to correct the drift weakening the navigation based on this technology. On the other hand, INS sensors can interact with the WiFi positioning system as they provide high-accuracy real-time navigation. A structure based on a Kalman filter and a particle filter is proposed. It fuses the heterogeneous information coming from those two independent technologies. Finally, the benefits of the proposed architecture are evaluated and compared with the pure WiFi and INS positioning systems. Copyright © 2006 Hindawi Publishing Corporation. All rights reserved.
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INTRODUCTION
Mobile positioning becomes of increasing interest for the wireless telecom operators. Indeed, many applications require an accurate location information of the mobile (context-aware application, emergency situation, etc.). While many outdoor solutions exist, based on GPS/AGPS, in indoor environments, the received signals are too weak to provide an accurate location using those technologies. Currently, given that many buildings are equipped with WLAN access points (shopping malls, museums, hospitals, airports, etc.), it may become practical to use these access points to determine user location in these indoor environments. Moreover, new regulations will impose to VoWiFi (voice over WiFi) operators to integrate a positioning solution in their terminals to comply with the E911 policy [1]. The location technique is based on the measurement of the received signal strength (RSS) and the well-known fingerprinting method [2, 3]. The accuracy depends on the number of positions registered in the database. Besides, signal fluctuations over time introduce errors and discontinuities in the user’s trajectory. To minimize the fluctuations of the RSS, some filtering is needed. A simple temporal averaging filter does not give satisfying results. Kalman filtering [4, 5] is commonly used in automatic control to track the trajectory of a target. However, more information can be used to improve the accuracy. In the following sections, we choose to use a map of the environment. It is used in order to find the most probable
trajectory of the mobile and avoid wall crossings. Including such information requires new filters as the Kalman filter is not adapted for this. Particle filters [6–8], based on MonteCarlo simulat
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