Research On Target Tracking In Wireless Sensor Network

Wireless Sensor Networks (WSN) is considered as one of the most significant emerging technologies in 21st century. Target tracking is an important application area of WSN which has caused wide public concern over the world. Target tracking has been widely used in the military environment, health care, transportation, etc. Its main purpose is to estimate the target trajectories by measuring the distance or the angle between target and sensor node.In this thesis, characteristics, architecture and application of wireless sensor networks are systematically analyzed, model and the tracking algorithm of the WSN are described in detail and tracking algorithm based on the particle filter is delved into. The tracking algorithms include the conventional particle filter, auxiliary particle filter and the expanded Kalman particle filter. Moreover, advantages and disadvantages of some algorithms are compared. Target tracking algorithm with mobile sensors based on unscented particle filtering is proposed to the issue of degeneration and sample problems in conventional particle filters.The biggest disadvantage in conventional particle filter is that the current observation data is not included in proposal distribution, which makes observations in the state estimation process play meaningless role in the amendment and lowers the performance of filtering algorithms. To solve this problem, we give a new algorithm which uses unscented Kaman filter to generate sophisticated proposal distributions from which samples is gotten. As a result, the problem of sampling from conversion priori density function is solved fundamentally, which improves the tracking accuracy effectively. Moreover, the number of sensors is largely reduced in tracking so that there is no need to deployment a large group of sensor nodes in network region which decreases the burden of heavy computations at the fusion center. At the same time, it avoids conflicts in information transmission, too much control signals, radio channel congestions and so on.Finally, a simulation example is given to proposed target tracking algorithm and some comparisons are made with conventional particle filter and extended Kaman particle filter target tracking algorithm. The simulation results show that proposed tracking algorithm saves network energy and improves tracking accuracy.