Fusion of WiFi, Smartphone Sensors and Landmarks Using the Kalman Filter for Indoor Localization
By Zhenghua Chen, Han Zou, Hao Jiang, Qingchang Zhu, Y. C. Soh, Lihua Xie. Italian National Conference on Sensors, 2015
393 citations recorded by the source corpus.
Location-based services (LBS) have attracted a great deal of attention recently. Outdoor localization can be solved by the GPS technique, but how to accurately and efficiently localize pedestrians in indoor environments is still a challenging problem. Recent techniques based on WiFi or pedestrian dead reckoning (PDR) have several limiting problems, such as the variation of WiFi signals and the drift of PDR. An auxiliary tool for indoor localization is landmarks, which can be easily identified based on specific sensor patterns in the environment, and this will be exploited in our proposed approach. In this work, we propose a sensor fusion framework for combining WiFi, PDR and landmarks. Since the whole system is running on a smartphone, which is resource limited, we formulate the sensor fusion problem in a linear perspective, then a Kalman filter is applied instead of a particle filter, which is widely used in the literature. Furthermore, novel techniques to enhance the accuracy of individual approaches are adopted. In the experiments, an Android app is developed for real-time indoor localization and navigation. A comparison has been made between our proposed approach and individual approaches. The results show significant improvement using our proposed framework. Our proposed system can provide an average localization accuracy of 1 m.
Subfields: Computer Networks and Communications, Electrical and Electronic Engineering, Ocean Engineering
View Paper: https://www.semanticscholar.org/paper/1543a3fc9df7c02cf0b90933f4308b4bd3c1dc69
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In Computer Networks and Communications
Citations in the corpus
- A research agenda for augmented and virtual reality in architecture, engineering and construction (2020, methodology): “Further research should focus on using a combination of methods, for example by leveraging WiFi signals [30, 180], Bluetooth beacons [196], and magnetic fields [161].”
- Wi-Fi Fingerprint-Based Indoor Positioning: Recent Advances and Comparisons (2016, neutral): “Fusing Wi-Fi fingerprints and inertial sensors based on Kalman filter has been studied extensively in works like [20], [87], [115]–[118].”
- WiFi CSI Based Passive Human Activity Recognition Using Attention Based BLSTM (2019, neutral): “for indoor localization [10].”