Abstract
With the increase of people’s work activities indoors, indoor positioning technology has become a hot spot in the field of positioning technology. The current mainstream indoor positioning includes WiFi, infrared, Bluetooth, ultra-wideband, ZigBee, RFID and ultrasonic technologies, each of which has its own advantages, but there are also certain shortcomings. The paper is based on WiFi localization technology, incorporating Pedestrian Dead Reckoning (PDR) localization technique and extending Kalman filter algorithm to solve the fused data. The experiments prove that the positioning error of the fused WiFi and PDR indoor positioning methods is smaller than that of the two technologies alone. The maximum error of the combined positioning method is 1.9532 m, the minimum error is 0.4727 m, and the mean error value is 0.8491 m. Compared with the two separate positioning methods, the accuracy of the Kalman filtered indoor positioning method fusing WiFi and PDR improved by 42.57% relative to PDR accuracy and 31.1% relative to WiFi accuracy, thus verifying the effectiveness of the Kalman filtered indoor positioning accuracy improvement by fusing WiFi and PDR.
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Funding
National Natural Science Foundation of China (41961063); Guangxi Natural Science Foundation – innovative Research Team Project (2019GXNSFGA245001).
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Lu, Y., Luo, S., Yao, Z., Zhou, J., Lu, S., Li, J. (2022). Optimization of Kalman Filter Indoor Positioning Method Fusing WiFi and PDR. In: Zu, Q., Tang, Y., Mladenovic, V., Naseer, A., Wan, J. (eds) Human Centered Computing. HCC 2021. Lecture Notes in Computer Science, vol 13795. Springer, Cham. https://doi.org/10.1007/978-3-031-23741-6_18
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