Abstract
For existing indoor localization algorithm has low accuracy, high cost in deployment and maintenance, lack of robustness, and low sensor utilization, this paper proposes a particle filter algorithm based on multi-sensor fusion. The pedestrian’s localization in indoor environment is described as dynamic system state estimation problem. The algorithm combines the smart mobile terminal with indoor localization, and filters the result of localization with the particle filter. In this paper, a dynamic interval particle filter algorithm based on pedestrian dead reckoning (PDR) information and RSSI localization information have been used to improve the filtering precision and the stability. Moreover, the localization results will be uploaded to the server in time, and the location fingerprint database will be built incrementally, which can adapt the dynamic changes of the indoor environment. Experimental results show that the algorithm based on multi-sensor improves the localization accuracy and robustness compared with the location algorithm based on Wi-Fi.
Highlights
The indoor positioning navigation system can provide navigation service for users in public places such as large complex buildings, and has wide application prospect [1] [2]
For existing indoor localization algorithm has low accuracy, high cost in deployment and maintenance, lack of robustness, and low sensor utilization, this paper proposes a particle filter algorithm based on multi-sensor fusion
There has been a growing interest in indoor positioning technology that relies on the existing senor, like the Wi-Fi, Zigbee, Pedestrian dead reckoning (PDR), Received signal strength indication (RSSI) and Radio Frequency Identification (RFID)
Summary
The indoor positioning navigation system can provide navigation service for users in public places such as large complex buildings, and has wide application prospect [1] [2]. RSSI positioning algorithm is simple, can provide absolute location information without adding additional hardware, the fusion algorithm based on PDR and RSSI has been widespread concern. This paper mainly studies a particle filter algorithm based on multi-sensor fusion indoor pedestrian localization, and combines the smartphone with the traditional positioning technology. The first step, the sensors built-in smartphone can predict the user’s movement and observation status, as Bayesian estimates of the movement model and observation model, and establish the fingerprint database of indoor environment. The last step, the localization results will be uploaded to the server in time, and the location fingerprint database will be built incrementally, which can adapt the dynamic transform of the indoor environment
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