Tracking in decentralised air-ground sensing networks

Abstract

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 the 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, steerable radar, multiple fixed radar arrays, and combined night vision (IR)-radar.