HMM and rule-based hybrid intruder detection approach by synthesizing decisions of sensors

Combining individual sensor decisions can be an effective way for the enhancement of the final decision on sensor fields for intruder detection. This paper proposes a novel methodology to unify the decisions from individual sensors on a sensor field through the (hidden Markov model) HMM and rules. The HMM especially provides a stochastic decision out of the individual sensor decisions on the sensor field; then it is filtered through rule inferences reflecting the knowledge of movement patterns on the level of the sensor field, such as spatial-temporal information and factual information on the movement of objects. This use of contextual knowledge remarkably improves the final decision for the detection. Also, this paper proposes the discretization method to express the state space of sensor field, and the performance evaluation is given by simulations.