基于改进卡尔曼滤波和参数拟合的矿井TOA定位方法
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摘要
针对矿井TOA(Time of Arrival)定位精度易受电磁波NLOS(Non Line of Sight)传播时延的影响,且不能满足井下应急救援、人员作业管理以及矿井物联网建设等需求的问题,通过对NLOS时延参考模型和矿井巷道设备运动特点的分析,将巷道电磁波NLOS传播时延分为随机NLOS时延和固定NLOS时延,结合两类NLOS时延造成测距误差的特点,提出了基于改进卡尔曼滤波和参数拟合的矿井TOA定位方法。为了消除由矿井巷道中机车等移动设备以及不规律设置设备引起的、具有随机性和难以定量分析等特点的巷道随机NLOS时延误差,设计了将新息阈值引入卡尔曼滤波器中,提高其系统对脉冲误差的滤除能力的方法;为了抑制由矿井巷道中固定设施及设备造成的具有稳定性的巷道固定NLOS时延误差,建立了巷道测距误差模型,构建了井下固有设备参数与定位估计值间的函数关系,通过参数拟合与投影几何算法来提高系统的定位精度。仿真结果显示,测量数据经过基于新息阈值的卡尔曼滤波器处理之后,误差曲线趋于平稳,定位误差保持在1.9～3.1 m,再经参数拟合和几何算法处理后,定位误差在0～0.8 m,平均误差由2.4 m降为0.3 m;且相比于SDSTWR(Symmetric Double-Sided Two-Way Ranging)方法、卡尔曼滤波和指纹定位方法以及卡尔曼滤波和参数拟合方法,所提方法平均定位误差分别减小了3.4,0.4和0.6 m。从而表明所提方法对TOA定位误差具有较明显的抑制作用,可以实现TOA方法在矿井NLOS环境中的有效应用。
The problem that the positioning accuracy of time of arrival(TOA) positioning method is susceptible to the delay of the non line of sight(NLOS) in propagation makes it difficult to meet the requirements of underground emergency rescue,personnel operation management and mine IoT(Internet of things) construction,etc. Based on the analysis of the reference model and the characteristics of the mine roadway equipment,the NLOS propagation delay of the roadway electromagnetic wave is divided into random NLOS delay and fixed NLOS delay. Combined with the characteristics of two kinds of NLOS delays,a method of mine TOA positioning based on improved Kalman filter and parameter fitting is proposed. In order to eliminate the random NLOS delay error in roadway caused by mobile equipment,such as locomotives in mine roadway,and irregular equipment and features by randomness and difficulty in quantitative analysis,a new threshold is designed and introduced into the Kalman filter to improve the system's ability in filtering the pulse error. Meanwhile,in order to suppress the stable NLOS delay error caused by the fixed facilities and equipment in the mine roadway,the roadway range finding error model is proposed,in which the functional relationship between the inherent equipment parameters and the positioning estimated value is established. Thus,the positioning accuracy of the system can be improved by using the parameter fitting and projection geometry algorithm. The simulation results show that after the measurement data is processed by the Kalman filter based on the threshold of innovation,the error curve tends to be smoother,and the positioning error is kept between 1.9 and 3.1 m. After being processed by the parameter fitting and geometric algorithm,the positioning error is between 0 and 0.8 m,and the average error is reduced from 2.4 m to 0.3 m. Compared with the other three methods,including the symmetric double-sided two-way ranging(SDS-TWR) method,the Kalman filter and fingerprint localization method,and the Kalman filter and parameter simulation,the average positioning error of the proposed method is reduced by 3. 4 m,0. 4 m and 0. 6 m respectively. It shows that the proposed method has a significant inhibitory effect on the TOA positioning error,and can effectively implement the TOA method in the mine NLOS environment.
The problem that the positioning accuracy of time of arrival(TOA) positioning method is susceptible to the delay of the non line of sight(NLOS) in propagation makes it difficult to meet the requirements of underground emergency rescue,personnel operation management and mine IoT(Internet of things) construction,etc. Based on the analysis of the reference model and the characteristics of the mine roadway equipment,the NLOS propagation delay of the roadway electromagnetic wave is divided into random NLOS delay and fixed NLOS delay. Combined with the characteristics of two kinds of NLOS delays,a method of mine TOA positioning based on improved Kalman filter and parameter fitting is proposed. In order to eliminate the random NLOS delay error in roadway caused by mobile equipment,such as locomotives in mine roadway,and irregular equipment and features by randomness and difficulty in quantitative analysis,a new threshold is designed and introduced into the Kalman filter to improve the system's ability in filtering the pulse error. Meanwhile,in order to suppress the stable NLOS delay error caused by the fixed facilities and equipment in the mine roadway,the roadway range finding error model is proposed,in which the functional relationship between the inherent equipment parameters and the positioning estimated value is established. Thus,the positioning accuracy of the system can be improved by using the parameter fitting and projection geometry algorithm. The simulation results show that after the measurement data is processed by the Kalman filter based on the threshold of innovation,the error curve tends to be smoother,and the positioning error is kept between 1.9 and 3.1 m. After being processed by the parameter fitting and geometric algorithm,the positioning error is between 0 and 0.8 m,and the average error is reduced from 2.4 m to 0.3 m. Compared with the other three methods,including the symmetric double-sided two-way ranging(SDS-TWR) method,the Kalman filter and fingerprint localization method,and the Kalman filter and parameter simulation,the average positioning error of the proposed method is reduced by 3. 4 m,0. 4 m and 0. 6 m respectively. It shows that the proposed method has a significant inhibitory effect on the TOA positioning error,and can effectively implement the TOA method in the mine NLOS environment.
引文
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[10]王明东,戴亚文,范俊,等.基于UWB技术的隧道无线定位方法研究[J].武汉理工大学学报,2016,38(8):56-60.WANG Mingdong,DAI Yawen,FAN Jun,et al.Research of wireless tunnel positioning method based on UWB Technology[J].Journal of Wuhan University of Technology,2016,38(8):56-60.
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[12]黄越洋,张嗣瀛,井元伟,等.基于TDOA和RSS的可行域粒子滤波非视距定位算法[J].控制与决策,2017,32(8):1415-1420.HUANG Yueyang,ZHANG Siying,JING Yuanwei,et al.Non-line of sight localization algorithm based on TDOA and RSS by feasible region particle filter[J].Control and Decision,2017,32(8):1415-1420.
[13]赵军辉,张雪雪,曾龙基.提高NLOS环境下室内定位精度的新方法[J].北京邮电大学学报,2012,35(6):38-43.ZHAO Junhui,ZHANG Xuexue,ZENG Longji.An approach to improving the accuracy of the indoor positioning with NLOS Environment[J].Journal of Beijing University of Posts and Telecommunications,2012,35(6):38-43.
[14]ZHOU Cheng,YUAN Jiazheng,LIU Hongzhe,et al.Bluetooth indoor positioning based on rssi and kalman filter[J].Wireless Personal Communications,2017,96(3):4115-4130.
[15]周瑞,袁兴中,黄一鸣.基于卡尔曼滤波的Wi Fi-PDR融合室内定位[J].电子科技大学学报,2016,45(3):399-404.ZHOU Rui,YUAN Xingzhong,HUANG Yiming.Wi Fi-PDR fused indoor positioning based on Kalman filtering[J].Journal of University of Electronic Science and Technology of China,2016,45(3):399-404.
[2]霍振龙.矿井定位技术现状和发展趋势[J].工矿自动化,2018,44(2):51-55.HUO Zhenlong.Status and development trend of mine positioning technology[J].Industry and Mine Automation,2018,44(2):51-55.
[3]SHAO Xiaoqiang,LI Kangle,CHEN Xi.A method of TOA positioning for mine to effectively reduce the impact of Non-Line of sight error propagation[A].Proceedings of the 30thChinese Control and Decision Conference,CCDC 2018[C].Shenyang:Institute of Electrical and Electronics Engineers Inc.,2018:4650-4655.
[4]KALPANA R,BASKARAN M.TAR:TOA-AOA based random transmission directed localization[J].Wireless Personal Communications,2016,90(2):1-14.
[5]WANG Peng,HE Jie,XU Liyuan,et al.Characteristic modeling of TOA ranging error in rotating anchor-based relative positioning[J].IEEE Sensors Journal,2017,17(23):7945-7953.
[6]赵泰洋,郭成安.基于接收信号强度的非视距检测与修正算法[J].数据采集与处理,2014,29(3):397-402.ZHAO Taiyang,GUO Cheng’an.NLOS detection and mitigation algorithm using received signal strength[J].Journal of Data Acquisition and Processing,2014,29(3):397-402.
[7]王洪,金尔文,刘昌忠,等.多点定位TOA精确估计及同步误差校正算法[J].系统工程与电子技术,2013,35(4):835-839.WANG Hong,JIN Erwen,LIU Changzhong,et al.Accurate estimation of TOA and calibration of synchronization error for multilateration[J].Systems Engineering and Electronic,2013,35(4):835-839.
[8]孙继平,李晨鑫.基于改进均值滤波和参数拟合的矿井TOA几何定位方法[J].煤炭学报,2015,40(5):1206-1212.SUN Jiping,LI Chenxin.Mine TOA geometric positioning method based on improved mean filtering and parameter fitting[J].Journal of China Coal Society,2015,40(5):1206-1212.
[9]孙继平,李晨鑫.基于卡尔曼滤波和指纹定位的矿井TOA定位方法[J].中国矿业大学学报,2014,43(6):1127-1133.SUN Jiping,LI Chenxin.Mine time of arrival positioning method based on Kalman filtering and fingerprint positioning[J].Journal of China University of Mining&Technology,2014,43(6):1127-1133.
[10]王明东,戴亚文,范俊,等.基于UWB技术的隧道无线定位方法研究[J].武汉理工大学学报,2016,38(8):56-60.WANG Mingdong,DAI Yawen,FAN Jun,et al.Research of wireless tunnel positioning method based on UWB Technology[J].Journal of Wuhan University of Technology,2016,38(8):56-60.
[11]CHENG Long,WANG Yan,SUN Xingming,et al.A mobile localization strategy for wireless sensor network in NLOS conditions[J].China Communications,2016,13(10):69-78.
[12]黄越洋,张嗣瀛,井元伟,等.基于TDOA和RSS的可行域粒子滤波非视距定位算法[J].控制与决策,2017,32(8):1415-1420.HUANG Yueyang,ZHANG Siying,JING Yuanwei,et al.Non-line of sight localization algorithm based on TDOA and RSS by feasible region particle filter[J].Control and Decision,2017,32(8):1415-1420.
[13]赵军辉,张雪雪,曾龙基.提高NLOS环境下室内定位精度的新方法[J].北京邮电大学学报,2012,35(6):38-43.ZHAO Junhui,ZHANG Xuexue,ZENG Longji.An approach to improving the accuracy of the indoor positioning with NLOS Environment[J].Journal of Beijing University of Posts and Telecommunications,2012,35(6):38-43.
[14]ZHOU Cheng,YUAN Jiazheng,LIU Hongzhe,et al.Bluetooth indoor positioning based on rssi and kalman filter[J].Wireless Personal Communications,2017,96(3):4115-4130.
[15]周瑞,袁兴中,黄一鸣.基于卡尔曼滤波的Wi Fi-PDR融合室内定位[J].电子科技大学学报,2016,45(3):399-404.ZHOU Rui,YUAN Xingzhong,HUANG Yiming.Wi Fi-PDR fused indoor positioning based on Kalman filtering[J].Journal of University of Electronic Science and Technology of China,2016,45(3):399-404.