A Data Fusion Algorithm for Large Heterogeneous Sensor Networks

Abstract

A distributed search based data fusion algorithm is presented for target detections in large heterogeneous sensor networks. A score function is introduced as the objection function during the optimal search. The network state is determined when the score is the highest. A close to optimal solution can be obtained before the arrival of the next sensor data thus enabling real time target tracking. The algorithm is evaluated with a series of real-time simulations on networks of variable sensor compositions with a commodity Linux cluster.