Underground precise positioning algorithm based on Kalman filter and weighted LM algorithm
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摘要: 针对基于UWB精确定位的井下近感检测装置定位结果易受非视距（NLOS）误差等噪声影响的问题，提出了一种基于卡尔曼滤波和加权LM法的井下精确定位算法。通过卡尔曼滤波预测过程得到标签卡坐标的先验估计值；利用几何关系计算估计坐标与各锚节点的距离，并将该距离与探测器直接测距值进行比较，根据差值分配各锚节点的测距权值；将权值矩阵和测距矩阵代入加权LM法中，得到标签卡坐标的中间结果；将中间结果作为测量值代入卡尔曼滤波更新过程中，得到标签卡的最终坐标。测试结果表明，与多边定位法相比，基于卡尔曼滤波和加权LM法的井下精确定位算法可在不影响定位速度的前提下，将定位精度提高一倍以上，有效降低了NLOS误差等噪声的干扰。Abstract: In view of problem that positioning result of underground proximity detection device based on UWB precise positioning is susceptible to noise such as non-line of sight (NLOS) error, an underground precise positioning algorithm based on Kalman filter and weighted LM algorithm was proposed. Priori estimation value of tag card coordinates is obtained by Kalman filter prediction process; distance between the estimatied coordinates and each anchor node is calculated by using geometric relationship, the calculated distance is compared with direct measuring value of the detector, and ranging weight of each anchor node is allocated according to difference of the calculated distance and measured distance; weight matrix and ranging matrix are substituted into the weighted LM algorithm as the measured value to obtain intermediate result of the tag card coordinates; the intermediate result is substituted into Kalman filter update process to obtain final coordinates of the tag card. The test results show that compared with the multilateral positioning method, the underground precise positioning algorithm based on Kalman filter and weighted LM algorithm can improve positioning accuracy by more than one time without affecting positioning speed, and effectively reducing the interference of NLOS error and other noises.
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