Research On Task Allocation And Filtering Techniques For Target Tracking Based On Wireless Sensor Network

Wireless sensor network (WSN) is a kind of intelligent network system with autonomous measurement and control ability, which is composed of thousands of wireless sensors generally densely distributed over the surveillance area without human participation. The sensor nodes are typically small in size, cheap and low cost with limited communication and computation abilities. The aim of WSN is to detect, gather and process the information in its surveillance area collaboratively and provide the information to users. Hence, WSN is important in battle field surveillance.Under the background of tracking flying targets such as airplane and ballistic missile in WSN, the multi-target tracking task allocation technique, the design of tracking filter algorithm and the trajectory parameter classification method of ballistic missile are studied in this thesis.Aiming at the task allocation of collaborative technique in WSN, a method for optimized task allocation based on elastic neural network is proposed. First a model of multi-coalition tracking multi-target is designed. Then disjoint fully connected subgraphs of neurons are constructed to solve the problem of optimized task allocation in tracking multi-target and the increment of system energy consumption when dynamic coalitions compete and conflict for the resource of sensor nodes. Compared with conventional method, simulation results show that the energy consumption of the system is reduced significantly during the process of tracking and the tracking accuracy is improved greatly when energy consumption of dynamic coalition is similar.When establishing accurate ballistic missile tracking model, trajectory parameter classification is the precondition. Aiming at trajectory parameter classification in WSN, the state model with unknown parameter is established at first. Then the multiple model maximum likelihood estimator is adopted to solve the parameter classification problem. Simulation results show that the classification performance is improved with the accumulated time increasing.Targeting at the application of tracking ballistic missile of WSN, in the condition of strong non-linear of state equation, sequential Monte Carlo particle filter based on Bayesian theorem is used to achieve estimation of target state. At first, the basic theory and disadvantages of particle filter are presented. Then the improved particle filter based on extended Kalman filter is designed to track ballistic target. Simulation results show that this method is preponderant and produces smaller error compared with classical method.