COLLABORATIVE TRACKING VIA PARTICLE FILTER IN WIRELESS SENSOR NETWORKS

Target tracking is one of the main applications of wireless sensor networks. Optimized computation and energy dissipation are critical requirements to save the limited resource of the sensor nodes. A framework and analysis for collaborative tracking via particle filter are presented in this paper. Collaborative tracking is implemented through sensor selection, and results of tracking are propagated among sensor nodes. In order to save communication resources, a new Gaussian sum particle filter, called Gaussian sum quasi particle filter, to perform the target tracking is presented, in which only mean and covariance of mixands need to be communicated. Based on the Gaussian sum quasi particle filter, a sensor selection criterion is proposed, which is computationally much simpler than other sensor selection criterions. Simulation results show that the proposed method works well for target tracking.

Acollaborative target tracking algorithm formultiple UAVs with inferior tracking capabilities

Target tracking is one of the hottest topics in the field of drone research.In this paper,we study the multiple unmanned aerial vehicles(multi-UAV)collaborative target tracking problem.We propose a novel tracking method based on intention estimation and effective cooperation for UAVs with inferior tracking capabilities to track the targets that may have agile,uncertain,and intelligent motion.For three classic target motion modes,we first design a novel trajectory feature extraction method with the least dimension and maximum coverage constraints,and propose an intention estimation mechanism based on the environment and target trajectory features.We propose a novel Voronoi diagram,called MDA-Voronoi,which divides the area with obstacles according to the minimum reachable distance and the minimum steering angle of each UAV.In each MDA-Voronoi region,the maximum reachable region of each UAV is defined,the upper and lower bounds of the trajectory coverage probability are analyzed,and the tracking strategies of the UAVs are designed to effectively reduce the tracking gaps to improve the target sensing time.Then,we use the Nash Q-learning method to design the UAVs’collaborative tracking strategy,considering factors such as collision avoidance,maneuvering constraints,tracking cost,sensing performance,and path overlap.By designing the reward mechanism,the optimal action strategies are obtained as the control input of the UAVs.Finally,simulation analyses are provided to validate our method,and the results demonstrate that the algorithm can improve the collaborative target tracking performance for multiple UAVs with inferior tracking capabilities.