Extended Object Tracking Based on Distributed Sensor Network

Abstract In this paper, an extended Kalman filtering algorithm based on coupled velocity model (CVM) is proposed for extended object tracking under distributed sensor networks. This algorithm tracks an object using measurements collected by multiple sensor nodes, and then obtains a global solution based on weighted Kullback-Leibler divergence. Simulation results display that the improved method can effectively track the extended object.