| 基于粒子群算法的传感器网络室内定位系统优化 |
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| 引用本文： | 张磊,黄如,袁伟娜. 基于粒子群算法的传感器网络室内定位系统优化[J]. 传感器与微系统, 2017, 36(4). DOI: 10.13873/J.1000-9787(2017)04-0061-04 |
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| 作者姓名： | 张磊 黄如 袁伟娜 |
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| 作者单位： | 华东理工大学信息科学与工程学院,上海,200237 |
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| 基金项目： | 国家自然科学基金资助项目，教育部基本科研业务基金资助项目，国家级大学生创新实践基金资助项目 |
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| 摘 要： | 通过接收信号强度指示(RSSI)和欧氏距离的非线性函数映射关系来进行弱移动目标的距离估计.以最大似然估计(MLE)法为基础,结合电磁波在自由空间中的传播特性,重新构建基于粒子群优化(PSO)定位算法的适应度函数来提高无线定位精度.并提出了一种惯性权重优化的自适应学习机制来优化全局搜索能力和局部搜索精度,提升定位算法的容错能力.测试结果表明:本室内定位算法具有抗干扰强、鲁棒性好和无线定位精度高等优点.
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| 关 键 词： | 接收信号强度指示(RSSI)测距 最大似然估计法 粒子群优化算法 无线定位 |
Optimization of indoor positioning system for sensor networks based on particle swarm optimization algorithm |
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| ZHANG Lei,HUANG Ru,YUAN Wei-na. Optimization of indoor positioning system for sensor networks based on particle swarm optimization algorithm[J]. Transducer and Microsystem Technology, 2017, 36(4). DOI: 10.13873/J.1000-9787(2017)04-0061-04 |
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| Authors: | ZHANG Lei HUANG Ru YUAN Wei-na |
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| Abstract: | The nonlinear function mapping relationship between RSSI and Euclidean distance is used to estimate distance of weak mobile target.This system improves positioning precision by rebuilding fitness function based on PSO positioning algorithm,on the basis of maximum likelihood estimation (MLE) and combines with characteristics of electromagnetic wave propagation in free space.Besides,an adaptive learning mechanism of inertia weight optimization is put forward to optimize global searching ability and local searching precision.The fault tolerance of localization algorithm is promoted.With experiment on CC2530,it is proved to have the advantages of strong antiinterference,good robustness and high precision of wireless localization. |
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| Keywords: | received signal strength indication(RSSI) maximum likelihood estimation(MLE) particle swarm optimization (PSO) algorithm wireless localization |
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