摘要
研究了山林火灾弱稀疏性检测问题,提出了一种基于无线传感器网络和智能优化压缩感知的山林火灾事件监测方案。利用无线传感器网络进行火灾报警信息采集,并采用压缩感知技术进行山林火灾事件分析检测。针对火灾事件向量稀疏度未知的特点,设计改进的离散粒子群优化算法,重新定义粒子编码方式和进化机制,并将该算法应用于压缩感知重构算法中。仿真结果表明与其他监测方案相比,该方案有效提高了山林火灾事件监测成功率,而且具有很强的时效性。
The problem of weak sparse detection for forest fire is studied,and a monitoring scheme for mountain forest fire incidents based on wireless sensor network(WSN)and intelligent optimization compression perception is proposed.The WSN is used to collect fire alarm information,and compressed sensing(CS)technology is used to analyze and detect forest fire events.As the fire incident vector sparsity is unknown,a new definition of the particle coding and evolution mechanism is proposed by the improved discrete particle swarm optimization(IDPSO)algorithm which is introduced to CS reconstruction algorithm.The simulation results show that,compared with other monitoring schemes,the success rate of mountain forest fire monitoring is effectively improved,and the timeliness is very strong.
作者
余阳
陈钦柱
赵海龙
韩来君
YU Yang;CHEN Qinzhu;ZHAO Hailong;HAN Laijun(Electric Power Research Institute,Hainan Power Grid Co.,Ltd.,Haikou 570311;Key Laboratory of Physical and Chemical Analysis of Hainan Power Grid,Haikou 570311)
出处
《微型电脑应用》
2019年第10期23-26,共4页
Microcomputer Applications
基金
海南电网有限责任公司电力科学研究院项目(073000KK52170004)
关键词
山林火灾
无线传感器网络
压缩感知
离散粒子群优化算法
监测
Forest fire
Wireless sensor network
Compressed sensing
Discrete particle swarm optimization algorithm
Monitoring
