在矿井地下巷道环境下,多径效应和非视距信号会造成TOA测距精度大幅下降。针对这一问题,在研究井下巷道扩频测距偏差产生机制的基础上,发现非视距信号与视距信号在接收端有相似的时延统计规律,据此,提出了一种适用于弯曲巷道的基于测距值二次重构的定位方法,由于矿井巷道的位置、走向是已知的,为巷道内移动目标的位置估计带来了许多有利的空间约束,因此,方法充分利用了巷道的位置信息,首先对弯曲巷道的NLOS测距值进行第1次重构以获得移动目标与相应基站直线距离的估计值,利用此重构值建立观测方程;接着第2次重构将观测方程的解点投影到巷道空间,获得位置估计点;最后,设计卡尔曼滤波器进一步优化位置估计点的定位精度,从而实现在更为一般的曲线巷道下的定位。仿真结果表明,方法在平均曲率为0~0.30的弯曲巷道中具有较为理想的定位精度;进而在平均曲率为0.192的某地铁人行弯曲通道所做的实测实验显示,方法定位均方根误差为0.891 m,误差累积分布理想,适用于井下人员精确定位。

The ranging accuracy will evidently decrease in underground roadway because of multi-path effect and NLOS signal. In order to solve this problem,the similar statistical law of time delay between NLOS signal and LOS sig- nal at their receiving ends is discovered based on the deviation generation mechanism of spread spectrum ranging of mine roadway. Thus a positioning method based on the two-reconstructing ranging value for curved roadway is presen- ted. Taking fully advantage of location information of roadways,this method firstly conducts the first reconstruction on the NLOS ranging value of curved roadways in order to get the estimate of linear distance between the moving target and the corresponding base station so the observation equation with the reconstruction figure can be established. Sec- ondly,this method carries out the secondary reconstruction and projects the solution point of observation equation to roadway space so that location estimate point can be obtained. Finally,Kalman filter is used to further optimize the po- sitioning accuracy of position estimation point and hence make it possible for positioning under a more general curved roadway. Simulation performance shows that the method can obtain a desired positioning accuracy in curved roadway with mean curvature from 0 to 0. 30. The experiment,which conducted in the pedestrian curved tunnel of a subway with a mean curvature 0. 192,shows that the Root Mean Square (RMS) of positioning error is 0. 891 m and the cumu- lative distribution of the error is ideal so that it is adoptable for accurate positioning of underground personnel.