Distributed tracking in sensor networks with limited sensing range

In this paper, we address the problem of distributed tracking of a maneuvering target using sensor networks with nodes that possess limited sensing range (LSR). In such sensor networks, a target can only be observed by a small percentage of the sensors and is practically hidden to the remaining majority of the nodes. This feature is shared among most of today's wireless sensor networks and differentiates them from their traditional counterparts involving data fusion for long-range sensors such as radars and sonars. Distributed Kalman filters have proven to be effective and scalable algorithms for distributed tracking in sensor networks. Our main contribution is to give a message-passing version of the Kalman-Consensus Filter (KCF)-introduced by the first author in CDC '07-that is capable of distributed tracking of a maneuvering target with a satisfactory performance. The architecture of this filter is a peer-to-peer (P2P) network of microfilters as extensions of local Kalman filters. The model proposed for the maneuvering target is a piece-wise linear switching system with two distinct modes of behavior that enables the target to stay inside a rectangular region in all time (for a bounded set of initial conditions). Simulation results are provided for a lattice-type sensor network with 100 LSR nodes tracking a target with switching modes of behavior which demonstrate the effectiveness of the proposed distributed data fusion and tracking algorithms.