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 microfllters 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.