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Decentralized and distributed autonomous sensing over networked sensor systems has many applications in surveillance, Internet of Things (IoT), autonomous cars, and UAV swarms tactics. In this study, we develop an average consensus-based decentralized data fusion approach for a target tracking application. Specifically, we extend the standard average consensus algorithm to merge the local state estimate information with that of the neighbors. We test the performance of our consensus based data fusion approach for various network configurations. We also perform numerical studies to compare the performance of our approach against the standard Bayesian data fusion approach. |
