文献类型：期刊文献

中文题名：扩展卡尔曼滤波的有向传感器网络移动目标跟踪算法

英文题名：Moving Target Tracking Algorithm Based on Extended Kalman Filter in Directional Sensor Networks

作者：吴冰[1]

机构：[1]新乡学院机电工程学院

第一机构：新乡学院机电工程学院

年份：2017

卷号：42

期号：5

起止页码：76-78

中文期刊名：火力与指挥控制

外文期刊名：Fire Control & Command Control

收录：CSTPCD;;北大核心:【北大核心2014】；CSCD:【CSCD_E2017_2018】；

基金：国家自然科学基金(61501391);河南省高等学校重点科技基金(15A510035);新乡市创新平台基金资助项目(CP1504)

语种：中文

中文关键词：传感器网络;目标跟踪;有向感知模型;EKF

外文关键词：sensor networks, target tracking, directional sensing model, EKF

摘要：针对传感器网络中跟踪目标需要大量的节点协同工作,还需要实时处理和传输大量数据,提出一种基于扩展卡尔曼滤波的有向传感器网络目标跟踪算法(EK-MTDC),重点研究了传感器网络中的扇区数量对节点间数据传输与目标跟踪精度的影响,根据对目标状态的分析,通过压缩参与监测的节点个数,选择激活网络中节点相交区域内的节点对跟踪目标进行监测。仿真结果表明,该算法能在不降低跟踪效果的前提下,降低网络能耗,延长其使用寿命。
For the target tracking in the sensor networks, a large amount of node need have a collaborative work with each other, at the same time, also need deal with and transfer a large amount data real-timely. In this paper, a moving target tracking algorithm based on extended Kalman Filter is proposed, This paper mainly studies on the effect of the data transmission between nodes and target tracking Accuracy with the number of sectors in the wireless sensor network. By compressing the number of nodes involved in the monitoring according to the analysis of the target state, then monitor the tracking target by the chosen nodes which are activated and intersected together in the network. Simulation results show that the algorithm can improved the life time and reduced the network energy wastage by the premise of guarantee of tracking accuracy.