The target tracking algorithm of mobile wireless sensor networks involves target motion trend prediction and subsequent node guidance. This study aims to solve the problems of global consistency of node information an...
详细信息
The target tracking algorithm of mobile wireless sensor networks involves target motion trend prediction and subsequent node guidance. This study aims to solve the problems of global consistency of node information and significant errors in forecasting fast-moving targets' trajectories through traditional distributed tracking methods in sensor networks. Initially, the average consistency algorithm is used to average the local measurements of each node to achieve global consistency. Then, semantic movingcomputing of the Internet of Things calculates and analyzes the node movement to support the subsequent movement guidance of nodes and target movement prediction. Finally, the simulation experiment is carried out to evaluate the commonly used target trajectory prediction model. The simulation results show that the node movement algorithm by average consistency can effectively improve the positioning accuracy of the network for moving targets. Besides, the positioning error decreases with the increase of the sensing radius R, the number of moving nodes nm, and the total number of nodes ns deployed in a particular range in a two-dimensional (2D) space. The positioning error after node movement in 2D space is about 20%-30%R lower than that in a static state. After node movement in a three-dimensional (3D) space, the positioning error is about 40%-50%R lower than in a dormant state. When the target moves at a speed greater than 7m/s, the consistency-based moving computing algorithm's target loss rate and tracking errors are about 0 similar to 10% and 1.5%similar to 2% lower than the target tracking algorithm via Kalman Filter. Therefore, the algorithm reported here can precisely track the high-speed moving target. The existing research on point target tracking has problems of insufficient accuracy and robustness. The algorithm proposed here has stronger robustness, reduced data error in multi-node, and more flexible node movements, providing a reference for the subsequen
暂无评论