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This paper presents the development of a passive infra-red sensor towerplatform along with a classification algorithm to distinguish between humanintrusion, animal intrusion and clutter arising from wind-blown vegetativemovement in an outdoor environment. The research was aimed at exploring thepotential use of wireless sensor networks as an early-warning system to helpmitigate human-wildlife conflicts occurring at the edge of a forest. There arethree important features to the development. Firstly, the sensor platformemploys multiple sensors arranged in the form of a two-dimensional array togive it a key spatial-resolution capability that aids in classification.Secondly, given the challenges of collecting data involving animal intrusion,an Animation-based Simulation tool for Passive Infra-Red sEnsor (ASPIRE) wasdeveloped that simulates signals corresponding to human and animal intrusionand some limited models of vegetative clutter. This speeded up the process ofalgorithm development by allowing us to test different hypotheses in atime-efficient manner. Finally, a chirplet-based model for intruder signal wasdeveloped that significantly helped boost classification accuracy despitedrawing data from a smaller number of sensors. An SVM-based classifier was usedwhich made use of chirplet, energy and signal cross-correlation-based features.The average accuracy obtained for intruder detection and classification onreal-world and simulated data sets was in excess of 97%.

Animation and Chirplet-Based Development of a PIR Sensor Array for Intruder Classification in an Outdoor Environment