Target tracking based on federated filter for mobile sensor networks

In this paper, we introduce a collaborative target tracking method which couples distributed estimation and motion control of mobile sensor networks. To estimate the target state, we develop a distributed federated filter (DFF) approach where only local communication between sensors is required. This DFF algorithm has superiority in tolerance of the target system noise and can acquire accurate estimation even if the model of the target is not accurate. We value information-distribution and information-fusion weights in the DFF according to the measurement situations of sensors. Then a flocking-based motion control method is proposed to track maneuvering targets. During the process of tracking, velocity matching and collision avoidance among mobile sensors are guaranteed. Finally, this coupled estimation and control algorithm is successfully simulated to track a linear-dynamics target with non-zero acceleration. Simulation results verify the performance and superiority of our method.