  This paper presents the development of a passive infra-red sensor tower
platform along with a classification algorithm to distinguish between human
intrusion, animal intrusion and clutter arising from wind-blown vegetative
movement in an outdoor environment. The research was aimed at exploring the
potential use of wireless sensor networks as an early-warning system to help
mitigate human-wildlife conflicts occurring at the edge of a forest. There are
three important features to the development. Firstly, the sensor platform
employs multiple sensors arranged in the form of a two-dimensional array to
give 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) was
developed that simulates signals corresponding to human and animal intrusion
and some limited models of vegetative clutter. This speeded up the process of
algorithm development by allowing us to test different hypotheses in a
time-efficient manner. Finally, a chirplet-based model for intruder signal was
developed that significantly helped boost classification accuracy despite
drawing data from a smaller number of sensors. An SVM-based classifier was used
which made use of chirplet, energy and signal cross-correlation-based features.
The average accuracy obtained for intruder detection and classification on
real-world and simulated data sets was in excess of 97%.
