摘要
为了减少煤矿井下环境对人员定位系统的影响,提出一种基于SVM分类的煤矿井下人员指纹定位算法,该算法由指纹数据库、井下巷道指纹数据采集和井下位置匹配等环节组成。该算法利用SVM分类方法建立指纹数据库,采用奇异值去除方法消除指纹动态影响,通过实时采样信号与指纹数据库进行映射的方法找出最佳匹配位置。通过随机采集50个指纹样点数据作为位置信息,进行多终端用户位置信息测量,并取5个终端用户的测量数据进行分析。定位试验表明,该算法定位误差小于1.5 m,相比传统的基于RSSI定位算法有更高的定位精度。
In order to reduce influence of underground environment on the positioning system,this paper presented a fingerprint positioning algorithm based on SVM classification.The algorithm consists of fingerprint database,underground tunnel fingerprint data acquisition and underground location matching.The SVM classification method was adopted to establish the fingerprint database,the singular value was re-moved to eliminate the effects of dynamic fingerprints,the best match position was found by matching the real-time sampling signal to the fingerprint database.This paper collected 50 samples by randomly fingerprint data as position information,muti-terminal user position infor-mation was measured and five user terminals measurement data were taken for analysis.The results of location experiments showed that the positioning error of proposed algorithm was less than 1.5 m,which was much better than traditional positioning algorithm based on RSSI.
出处
《煤炭科学技术》
CAS
北大核心
2014年第11期73-76,共4页
Coal Science and Technology
基金
国家自然科学基金资助项目(61071087)
中国煤炭工业协会资助项目(MTKJ2011-363)
山东省自然科学基金资助项目(ZR2011FM018)
山东省博士后创新资助项目(201103099)
关键词
人员定位
煤矿井下
RSSI定位算法
指纹定位算法
person positioning
underground mine
RSSI positioning algorithm
fingerprint positioning algorithm
