Credit-Based Multiple Human Location for Passive Binary Pyroelectric Infrared Sensor Tracking System: Free From Region Partition and Classifier

We present a credit-based multiple human location method for passive binary pyroelectric infrared sensor tracking system. Different from the traditional Fresnel-lens pyroelectric infrared (PIR) system, we use a special optical cone to design a small and flat PIR sensor network system and propose a petal-shape sensing model for sensor node. The angular bisector of one petal is a detection line and all intersection points of activated detection lines are measurement pointes. We return to the essence of sensing and propose the credit to represent the probability of measurement points falling into the field of view of the sensors. Multiple human targets can be located by cluster analyzing and data association with the highest credit, which makes the human location free from region partition and region classifier. The simulation and experimental results show that the proposed location method has improved the location accuracy, and meanwhile, reduced the execution time and the dependence on the layout of sensor network.