A new Wi-Fi/ GPS fusion method for robust positioning in urban environments

Alfakih, Marwan; Keche, Mokhtar; Benoudnine, H.

doi:10.1016/j.phycom.2018.09.003

Cited by 8 publications

(5 citation statements)

References 19 publications

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“…This demonstrates the robustness of the KF‐based fusion approach with respect to the selected Wi‐Fi fingerprinting method. In addition, comparing these results with those obtained using the PFMM fusion method in [16] shows that the proposed KF‐based fusion achieves better performances (1.80 and 1.76 m against 2.26 and 2.00 m).…”

Section: Performance Evaluationmentioning

confidence: 82%

“…For a further evaluation of the performance of the proposed method, two other fusion methods were implemented in the present work, and their performances were compared with that of the KF-based fusion method. These methods are the KF state fusion method [32,33] and the Particle Filter Multiple Models (PFMM) fusion method [16].…”

Section: The Experimental Resultsmentioning

confidence: 99%

“…This demonstrates the robustness of the KF-based fusion approach with respect to the selected Wi-Fi fingerprinting method. In addition, comparing these results with those obtained using the PFMM fusion method in [16] True path GPS Wi-Fi Proposed KF-based fusion FIGURE 13 Estimated trajectories obtained with different location methods using 1000 Wi-Fi RSS samples The results presented so far were obtained using only 100 RSS samples in the calibration phase of the fingerprinting technique. To enhance these results, we have increased this number to 1000 RSS samples.…”

Section: The Experimental Resultsmentioning

confidence: 99%

“…On the other hand, Zampella et al [15] used a two-level structure with a low-level pedestrian dead recognition (PDR) filter and a high-level PF to combine the PDR and RSS measurements for indoor positioning. In [16], we have used two PFs and a multiple-model approach to integrate the estimates coming from the GPS and Wi-Fi positioning systems. Indeed, the GPS and Wi-Fi are seen as two models, which interact by exchanging a part of their particles.…”

Section: Literature Reviewmentioning

confidence: 99%

“…To assess the performances of the KF‐based fusion method, we used the same experiment that we have used in [16]. This experiment was carried out in the Electronics Department, University of Science and Technology of Oran.…”

Section: Performance Evaluationmentioning

confidence: 99%

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A Kalman‐filter‐based fusion method for accurate urban localisation

2021

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The outage and degradation of the global navigation satellite system (GNSS) signals caused by the multipath phenomena reduce the location accuracy of these systems in urban environment. Hence, integrating an additional localisation technology with the GNSS, so that each technology complements the weakness of the other one, is an efficient solution to improve this accuracy. The widespread availability of the Wi-Fi technology makes it the most appropriate additional technology. In this work, a fusion algorithm based on a Kalman filter is used to integrate the GPS localisation with Wi-Fi fingerprinting localisation in urban environment. The fusion algorithm uses the positions delivered by these two systems to achieve an accurate estimation of the mobile position. The experimental results show that the performance of the proposed fusion method is more accurate than those of the individual methods and other fusion methods from the literature. 1 INTRODUCTION Global navigation satellite systems (GNSSs) are intended to provide positioning in outdoor. They can achieve a reasonably good positioning in open environments. However, the positioning accuracy is reduced in the urban environments, due to reflections of satellite signals and the Non-Line-Of-Sight (NLOS) reception. The frequently outages and the multipath phenomena that affect the satellite signals can degrade the performance of positioning. In these situations, several alternative technologies can be used, such as the inertial navigation systems (INS) and the radio frequency (RF)-based systems, such as RF identification, ultra wideband (UWB), Bluetooth and Wi-Fi [1,2]. However, the drawback of these technologies is that they are not available everywhere, unlike the GNSS. Moreover, each one has its own limitations in such situations. Therefore, it is possible to take advantage of the indoor technologies that can cover urban areas, and combine them with GNSSs to build an accurate localisation system that works everywhere. Wi-Fi technology is commonly used to achieve positioning inside buildings, due to its availability in such areas, its low cost and its easy implementation. In addition, the widespread of the Wi-Fi signals over long distances makes it able to cover urban areas outside buildings. However, localisation using This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Section: Performance Evaluationmentioning

confidence: 82%