Event-triggered filtering with application to target tracking in binary sensor networks
Sangjin Lee, Weiyi Liu, Inseok Hwang · 2012
This paper presents an event-triggered filtering algorithm with application to target tracking in binary sensor networks. The target is modeled by a stochastic differential equation and the binary sensors provide one-bit information about the target's presence or absence within the sensing range. The sensors are triggered when the target enters or leaves the sensor's sensing range. Based on the sensor model, target tracking problem is formulated as a filtering problem where the state of the stochastic dynamic system is estimated using only two types of measurement data: the times when the sensors are triggered and the positions of the triggered sensors. The event-triggered filtering problem is then solved by a proposed algorithm based on a Markov chain approximation method.