基于RSS手指模的煤矿井下WLAN定位方法  

Underground coal mine WLAN localization algorithm based on RSS fingerprinting

作　　者：郝丽娜[1] 张秀均[1] 郁万里[1] 乔莹[1] 

机构地区：[1]中国矿业大学物联网(感知矿山)研究中心信息与电气工程学院,江苏徐州221008

出　　处：《传感器与微系统》2012年第9期46-49,共4页Transducer and Microsystem Technologies

基　　金：国家自然科学基金资助项目(60972059)

摘　　要：到达信号强度(RSS)手指模定位技术已广泛应用于室内定位技术,提出了适用于煤矿井下由于电磁波多径效应而变得复杂的环境的RSS手指模定位算法。通过对煤矿井下电磁环境信息的采集,对采集到的信息进行处理,使用贝叶斯公式法估计出概率较大的3个位置,然后再使用最邻近法的欧几里德距离估计出位置。通过对实验数据的统计分析,仿真结果表明:提出的基于RSS手指模改进的融合算法的节点定位精度要比K邻近法的定位精度要高,定位性能要优越。Received signal strength （RSS）fingerprinting is widely used in the indoor localization technology. The RSS finger mode localization algorithm is put forward to use in the complex underground mine environment. Through the information of electromagnetic environment, the collected information is processed, the larger three probability position is estimated using Bayesian formula method, and the location is estimated using Euclidean distance of the nearest formula. By the statistical analysis on experiment data, the simulation results show that the localization precision of the advanced algorithm based on RSS fingerprinting is better than the K nearest algorithm, and its：performance is superior.

关 键 词：无线保真 到达信号强度 定位 手指模 

分 类 号：TP929.4[自动化与计算机技术]