access icon free Detection of multi-occupancy using device-free passive localisation

Indoor device-free passive localisation (DfPL) technology uses a received signal strength indication (RSSI)-based method to record variances of a measured signal where a person being tracked is not carrying any electronic device that can be used to estimate the location. The system monitors the changes in the RSSI measurements caused by the presence of a human body in an indoor environment. For example, it is known that the resonance frequency of water is 2.4 GHz and the human body contains >70% water. Thus, the human body attenuates the wireless signal reacting as an absorber. Wireless communication signal strengths between a number of nodes, using IEEE 802.11 or 802.15.4 standards, show that communication links covering distinct areas cannot be affected simultaneously by only one person. Thus, the authors have deployed a novel system that can identify multi-occupants in an environment using patterns of motion from those monitored areas. A pattern recognition neural network was used to identify two people in the environment. No other work based on the DfPL technique has focused on multi-occupancy.

Inspec keywords: personal area networks; object tracking; radio links; wireless LAN; object detection; indoor radio; IEEE standards; sensor placement; neural nets

Other keywords: motion patterns; people identification; location estimation; received signal strength indication; RSSI measurement; pattern recognition neural network; person tracking; communication links; device free passive localisation; IEEE 802.15.4 standards; indoor environment; wireless communication signal strength; IEEE 802.11 standards; multioccupancy detection; indoor DfPL technique

Subjects: Local area networks; Neural computing techniques; Computer communications; Signal detection; Wireless sensor networks

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