基于UKF的深井监测移动节点定位算法

针对线性系统理论的监测定位技术误差较大且又无法实时监测深井人员及移动设备的位置问题，提出一种基于非线性函数不敏卡尔曼滤波（UKF）移动节点定位算法（U-MPA）。在建立U-MPA监测系统及巷道模型的基础上，采用UKF方法对RSSI滤波测距，通过局部坐标系，实现对移动节点实时定位监测；同时，通过改变锚节点间距密度，实现不同定位精度要求。仿真实验表明:U-MPA算法相比RSSI算法定位误差有明显减小，U-MPA算法的平均定位偏差为RSSI算法的44%。

Positioning algorithm for mobile nodes monitoring in deep mine based on UKF

Aiming at the problems that the monitoring positioning technology of linear system theory has a large error and can’t monitor the positions of personnel and mobile equipments in deep mine in real-time, a positioning algorithm for mobile nodes based on the nonlinear function unscented kalman filter (UKF) was put forward, namely U-MPA. On the basis of establishing the U-MPA monitoring system and the roadway model, the UKF method was applied to filter ranging for RSSI, and the positioning monitoring of the mobile nodes in real-time was realized through the local coordinate system. Meanwhile, different positioning accuracy requirements were achieved by changing the spacing density of anchor node. The simulation experiments showed that the positioning error of U-MPA algorithm was significantly smaller compared with RSSI algorithm, and the average positioning deviation of U-MPA algorithm was 44% of that by RSSI algorithm.