CAJ | 학술논문

煤矿井下环境复杂多变，对人员精确定位技术挑战很大。目前矿井巷道中采用基于接收信号强度指示 RSSI ( Received Signal Strength Indication)的位置指纹定位算法存在定位目标漂移、抖动和定位精度不高等问题。提出一种改进的指纹定位匹配算法，该算法将K邻近算法和最短历史路径匹配法联合并利用速度限定位置估计补偿算法对定位精度进行修正。利用在煤矿巷道中的实测数据，对改进的匹配算法进行了验证与误差分析。仿真结果表明，改进后的算法能够提高定位精度，满足矿井人员定位、目标跟踪和目标轨迹查询等要求。
매광정하배경복잡다변，대인원정학정위기술도전흔대。목전광정항도중채용기우접수신호강도지시 RSSI ( Received Signal Strength Indication)적위치지문정위산법존재정위목표표이、두동화정위정도불고등문제。제출일충개진적지문정위필배산법，해산법장K린근산법화최단역사로경필배법연합병이용속도한정위치고계보상산법대정위정도진행수정。이용재매광항도중적실측수거，대개진적필배산법진행료험증여오차분석。방진결과표명，개진후적산법능구제고정위정도，만족광정인원정위、목표근종화목표궤적사순등요구。
The environment of coal mine is very complicated,which requires great accuracy of personnel positioning. Currently,in the mine roadway,the location of the fingerprint localization algorithm based on RSSI( Received Signal Strength Indication) has some deficiencies, such as targeting drift, jitter, and low positioning accuracy. This paper proposes a modified matching localization algorithm. This algorithm combines K nearest neighbor algorithm with the shortest historical path matching and uses speed-limited location estimation compensation algorithm to correct the positioning accuracy. Based on the authentic-measured data from the coal mine tunnel, this article verifies and analyses the error for the improved matching algorithm. The simulation results show that the improved matching algorithm can improve positioning accuracy and meet the requirements of mine personnel positioning, target tracking,and target trajectory querying.