Tracking in decentralised air-ground sensing networks

This paper describes the theoretical and practical development of a decentralised air and ground sensing network for target tracking and identification. The theoretical methods employed for studying decentralised data fusion problems are based on the information-filter formulation of the Kalman filter algorithm and on information-theoretic methods derived from Bayes theorem. The paper particularly focuses on how these methods are applied in very large heterogeneous sensor networks, where there may be a significant amount of data delay or corruption in communication. This paper then describes the development of a practical system aimed at demonstrating some of these principles. The system consists of a number of Unmanned Air Vehicles (UAVs), with radar and vision payloads, able to observe a number of ground targets. The UAV sensor payloads are constructed in a modular fashion, with the ability to communicate in a network with both other air-borne and other ground sensors. The ground sensor system comprises of multiple modular sensing nodes which include vision, scanned laser, steer-able radar, multiple fixed radar arrays, and combined night vision (IR)-radar.