Enhanced Multiplex Binary PIR Localization Using the Transferable Belief Model

Pyro-electric infra red (PIR) sensors have been widely used in different indoor localization applications during the last decade. These sensors are cheap, non-intrusive, and non-wearable, nevertheless, the binary PIR sensor detects only the presence of a human motion in its field of view without any other information about the actual location. Therefore, to localize a person in different zones of interest, the use of several PIR sensors with overlapping field of view is necessary. To reduce the number of sensors used, we use multiplex masks with the binary PIR sensors to obtain a compressed overlapping structure. Such a structure induces ambiguity during transitions between zones. In this paper, we show how to circumvent this issue by using a novel localization algorithm based on the transferable belief model TBM. Besides, we show how to tune efficiently the parameters of our algorithm, by choosing an appropriate discounting factor within (TBM). Experiments using standard commercialized sensors equipped with the multiplex masks emphasize the performance of our novel method.