Research On Target Tracking For Wireless Sensor Networks In Complex Environment

With the rapid development of wireless communication technology,microelectromechanical systems,low-power embedded devices and on-chip systems,sensorintegrated wireless processor nodes can be produced at a lower cost and can be widely deployed in detection scenarios and resulted in large-scale network.Wireless sensor network(WSNs)have been widely used in the military,civil and other fields.Considering the low cost,low power consumption,small size of wireless sensor nodes and the high redundancy,high versatility of WSNs,WSNs can be widely deployed in environments to obtain datas where human beings are not suitable to reach.In this paper,multi-objective SMC-PHD filtering algorithm is applied to WSNs with 360 ° pure distance ultrasonic array sensor.A new method is proposed to eliminate the redundant newborn particles in the complex and disorderly measurement data.It solves the influence from newborn particles on the surviving target estimations and improves the algorithm of resampling step while guaranteeing the number of particles,increasing the diversity of particles and avoid the problem of particle degeneration.The labeling method is used in the state estimation step that divide the different particles into different labels according to the number of targets to solve the particle accumulation problem when the trajectories intersect.Aiming at the high complexity of particle filter,the application of BOX-PHD algorithm is realized in order to satisfy the real-time requirements.