基于TMR磁传感器的位置感知与跟踪
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摘要
基于TMR(tunnel magnetic resistance)磁传感器良好的线性度,提出一种通过多TMR传感器测距感知磁体的平面位置和移动轨迹的方法。首先,利用LabVIEW和Arduino获取单一TMR传感器的测距性能曲线,再通过平面内部多个TMR传感器检测获取磁场强度的指纹特征矩阵,最后利用加权K近邻算法(WKNN)估计磁体实时位置并绘制移动轨迹。在构建的检测装置上实测,结果表明:采用该方法的位置测量系统在10cm×10cm范围内平均定位误差约为1mm,均方根误差RMSE=0.3。
Based on the good linearity of the TMR(tunnel magnetic resistance)magnetic sensor,a method for sensing the plane position and moving trajectory of the magnet through multi-TMR sensor ranging is proposed.First,Using LabVIEW and Arduino to get the ranging performance curve of single TMR sensor,and then obtaining fingerprint feature matrix by detecting multiple TMR sensors arranged in the plane.Finally,weighted K nearest neighbor algorithm(WKNN)is used to estimate the real time position of the magnet and draw the trajectory through LabVIEW.A actual monitoring device is built and measured,and,the experimental results show that the location error of the position measurement system is about 1 mm and the root mean square error is RMSE=0.9 mm in the 10 cm×10 cm range.
Based on the good linearity of the TMR(tunnel magnetic resistance)magnetic sensor,a method for sensing the plane position and moving trajectory of the magnet through multi-TMR sensor ranging is proposed.First,Using LabVIEW and Arduino to get the ranging performance curve of single TMR sensor,and then obtaining fingerprint feature matrix by detecting multiple TMR sensors arranged in the plane.Finally,weighted K nearest neighbor algorithm(WKNN)is used to estimate the real time position of the magnet and draw the trajectory through LabVIEW.A actual monitoring device is built and measured,and,the experimental results show that the location error of the position measurement system is about 1 mm and the root mean square error is RMSE=0.9 mm in the 10 cm×10 cm range.
引文
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[2]KALATHIL S,NARASIMHAN K S.Wireless Magnetic Position Sensor[P].United States Patent,US9182459B2.
[3]YUAN X A.Circumferential current field testing system with TMR sensor array for non-contact detection and estimation of cracks on power plant piping[J].Sensors and Actuators A:Physical,2017,15(263):542-553.
[4]FRANCIS L A,POLETKIN K.Magnetic Sensors and Devices:Technologies and Applications[M].CRC Press,2017.
[5]李东昇,程武山.基于TMR磁传感器的电力系统大电流测量[J].传感器与微系统,2013,32(12):131-134,141.
[6]吕华,等.隧道磁电阻(TMR)磁传感器的特性与应用[J].磁性材料及器件,2012(3):1-4.
[7]万舟,单阳,魏健雄.隧道磁电阻传感器在钢丝绳检测系统中的应用[J].软件导刊,2018(4):171-174.
[8]爱松,付跃文,杨浩.小径管周向裂纹脉冲涡流检测仿真和试验研究[J].失效分析与预防,2017,12(2):71-77.
[9]TIAN X,SHEN R,LIU D,et al.Performance analysis of RSS fingerprinting based indoor localization[J].IEEE Transactions on Mobile Computing,2017,16(10):2847-2861.
[10]Li C,QIU Z,LIU C.An improved weighted K-nearest neighbor algorithm for indoor positioning[J].Wireless Personal Communications,2017,96(2):2239-2251.
[11]唐瑞,孙冰洁,周礼争,等.基于RSSI的KNN-PIT室内自适应定位算法[J].传感器与微系统,2015,34(7):128-131.
[12]刘修泉,李艳红,汪小红,等.基于LabVIEW的弱磁信号采集系统设计[J].机电一体化,2016,22(6):65-68.
[13]谢作孟.基于TMR传感器的车辆检测与识别技术研究[D].杭州:杭州电子科技大学,2015.
[14]田洪亮,钱志鸿,梁潇,等.离散度WKNN位置指纹Wi Fi定位算法[J].哈尔滨工业大学学报,2017,49(5):94-99.
[15]毛永毅,杨强强,徐萍.基于WiFi的一种改进的室内定位算法[J].测控技术2017,36(8):26-30.
[16]赵钟伟.基于磁场信息的定位原理及应用[D].杭州:浙江大学,2014.