Research on Scalable Consensus Algorithm of Target Tracking for Wireless Sensor Network

A scalable dynamical average consensus kalman filter algorithm is proposed for improve performance of maneuver target tracking and reduce energy consumption of wireless sensor network.Dynamic clusters were organized according to the predicted position of the target,and multi-sensors work cooperatively for target detection,distributed state estimation.Three scalable dynamical consensus kalman filter,i.e.based on observation,observation innovation and estimate,are employed for distributed state estimation.Cluster head is not fusion center,but a ordinary node for choose tasking sensor of next step and forwards current state estimate and corresponding error covariance.Each tasking sensor only requires information from neighboring nodes and needs only communicate with one-hop neighbors in a communication network,resulting in decrease communication consumption and fully scalable algorithm.Simulation results show that,compared with central kalman filter algorithm,observation,innovation and estimate based consensus distributed kalman filter will get comparable accuracy while it will be more robust due to its distributed manner.