Decentralized algorithms and architecture for tracking and identification

Present two algorithms for tracking and identification in decentralized multi-sensor systems. Decentralized architectures have many benefits in terms of modularity, speed and robustness. The state estimation (tracking) algorithm is a decentralized Kalman filter (DKF) based on the extended Kalman filter. Identification is achieved by the decentralized Bayesian identification (DBI) algorithm, which identifies targets being tracked. For each of the algorithms The authors discuss optimality and the effect of reducing connectivity. The structure of the algorithms leads to the development of an architecture for a modular sensing node based on communication considerations. The authors present example implementations of both algorithms on actual transputer-based sensing nodes. They describe RCD (region of constant depth) tracking and for this develop a monopulse sonar arrangement with which they implement real-time autonomous tracking by a single sensor node. The second example implementation describes identification of targets being tracked by the DKF using the DBI on actual CCD camera-based nodes