摘要
针对现有矿用精确定位系统存在功耗高、数据产生率大的问题,设计了一种矿用精确定位系统认知算法。该认知算法通过感知目标节点移动速度、位置的变化,采用重构决策的方法将目标节点的发射功率和定位数据采样间隔进行合理重配置,实现系统功耗和数据产生率的优化。仿真结果表明,该认知算法能有效地降低矿用精确定位系统的功耗和数据产生率,同时不会影响定位系统对节点运动轨迹的跟踪性能。
In view of problem of high power consumption and data generation rate of existing mine-used accurate positioning system, a cognitive algorithm for mine-used accurate positioning system was designed. The recognition algorithm reasonably recontigures transmitter power of target node and location data sampling interval by sensing changes of moving speed and position of target node and using reconstruction method of decision-making, achieves optimization of system power and data generation rate. The simulation results show that the cognitive algorithm can effectively reduce power consumption and data generation rate of accurate positioning system without affecting track performance of positioning system to node trajectory.
出处
《工矿自动化》
北大核心
2015年第8期60-64,共5页
Journal Of Mine Automation
基金
重庆市教委科学技术研究资助项目(KJ1403208)
关键词
煤矿
精确定位
认知算法
功耗
数据产生率
coal mine
accurate positioning
cognitive algorithm
power consumption
data generation rate
