基于CSI的轻量级自适应井下定位算法

针对传统井下定位成本高、工作危险系数大的问题,提出一种基于信道状态信息(channel state information, CSI)的轻量级自适应井下定位(lightweight self-adaptive underground positioning algorithm, LSA)方法。LSA方法以细粒度的CSI替代粗粒度的接收信号强度(received signal strength indicator, RSSI)来获得更高的定位精度,采用逆傅里叶变换将原始CSI数据转换为信道脉冲响应,以此选取视距信号,并通过构建CSI视距信号衰减模型实现轻量级的精确测距;基于井下现有WiFi网络中的访问接入点(access points, APs)位置和井下巷道特征,计算目标相对AP的方向,根据方向和测距结果完成定位。该方法能够自适应于AP在巷道中的任意位置部署,并利用拐角识别优化算法进一步提高定位的精度。试验结果表明,该方法能够使得定位中位数误差达到0.53 m,且无需在井下单独部署任何定位系统,性能明显优于已提出的CDPF、FILA等其他定位算法。

Lightweight self-adaptive CSI-based positioning algorithm in underground mine

To solve the problem of high cost and working hazard factor of traditional downhole positioning methods, a lightweight self-adaptive CSI-based positioning algorithm in underground mine was proposed. The fine-grained CSI was used to obtain higher positioning accuracy rather than coarse-grained RSSI, inverse fast Fourier transform was adopted to transform CSI data to channel impulse response so as to get the line-of-sight signal, an attenuation model of line-of-sight signal of CSI was built to implement accurate ranging, position features of existing point access points (APs) in wireless fidelity and characteristics of rock roadways was utilized to calculate orientation of target relative to AP, which finally completed location according to orientation and distance. LSA was adaptive to arbitrary deployment modes, and the corner recognition optimization algorithm was used to improve positioning accuracy. The experimental results showed that LSA method median error could reach 0.53 m and eliminate the need to deploy any positioning system in the well alone, the performance was superrior to CDPF and FILA.