摘要
为解决经典定位算法应用在输电线路走廊火灾监控时定位精度低、噪声影响大的问题,提出了一种架空输电线路山火监控定位算法。在求解未知节点到锚节点距离时,引入反向传播(BP)神经网络,以信号接收强度和最小跳数为输入量,建立距离预测模型,然后采用双曲线法估算未知节点坐标。通过与传统DV-Hop与接收信号强度指示(RSSI)算法进行仿真实验比较,该算法具有更好的定位精度,对环境噪声也有较强的抗干扰能力,从而有效保障了架空线路山火监控定位的精确性。
In order to solve the problems of low positioning precision and large noise impact when the classic positioning algorithm is applied to fire monitoring of transmission line corridors,a mountain fire monitoring and positioning algorithm for overhead transmission lines is proposed.When solving the distance from the unknown node to the anchor node,the BP neural network is introduced,the signal reception strength and the minimum number of hops are used as inputs to establish the distance prediction model,and then the hyperbolic method is used to estimate the coordinates of the unknown node.Compared with the traditional DV-Hop and received signal strength indication(RSSI)algorithms,the algorithm has better positioning accuracy and stronger anti-interference ability to environmental noise,thus effectively ensuring the accuracy of overhead fire monitoring and positioning.
作者
杨佳
文斌
许强
YANG Jia;WEN Bin;XU Qiang(School of Electrical and Electronic Engineering,Chongqing University of Technology,Chongqing 400054,China;School of Computer Science and Information Engineering,Chongqing Technology and Business University,Chongqing 400067,China)
出处
《传感器与微系统》
CSCD
北大核心
2022年第6期126-129,共4页
Transducer and Microsystem Technologies
基金
重庆市科委自然科学基金资助项目(CSTC2012JJA40061)
重庆市教委科学技术研究计划资助项目(KJ130834)
重庆市教委科学技术研究计划资助项目(KJ1500619)
重庆理工大学研究生创新项目(ycx20192055)。
关键词
无线传感器网络
输电线路走廊
山火监控
反向传播神经网络
定位
wireless sensor networks(WSNs)
transmission line corridor
mountain fire monitoring
back propagation(BP)neural network
localization
