Title :
Distributed tracking in sensor networks with limited sensing range
Author :
Olfati-Saber, Reza ; Sandell, Nils F.
Author_Institution :
Dartmouth Coll., Dartmouth, NH
Abstract :
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.
Keywords :
Kalman filters; message passing; peer-to-peer computing; piecewise linear techniques; sensor fusion; target tracking; tracking filters; wireless sensor networks; data fusion; distributed Kalman filter; distributed tracking problem; limited sensing range; message passing version; microfllters; peer-to-peer network; piece-wise linear switching system; wireless sensor network; Educational institutions; Filtering algorithms; Filters; Network topology; Peer to peer computing; Radar tracking; Sensor fusion; Sonar; Target tracking; Wireless sensor networks; Kalman-Consensus filtering; distributed data fusion; sensor networks; target tracking;
Conference_Titel :
American Control Conference, 2008
Conference_Location :
Seattle, WA
Print_ISBN :
978-1-4244-2078-0
Electronic_ISBN :
0743-1619
DOI :
10.1109/ACC.2008.4586978