磁传感器输出姿态信息修正方法研究
|
摘要
在基于地磁、角速度及重力加速度(MARG)测量的航姿融合估计应用中,磁干扰会破坏磁传感器输出所含姿态信息,进而降低姿态估计精度。为提高姿态估计的抗磁干扰能力,研究了在静态及小幅加速度运动条件下修正磁传感器输出的方法。该方法利用重力加速度测量值对归一化磁传感器输出向量进行修正,修正依据是地磁向量与重力加速度夹角恒定以及最小化修正角度。分析了航姿融合估计算法模型与磁传感器输出修正原理,给出了修正方程并推导了修正计算式,最后通过静态与动态实验验证了修正效果。实验表明,磁传感器输出修正使俯仰角及横滚角估计均方根误差分别降低2.6°与1.6°。此外,该修正方法还具有不改变融合计算过程,便于与其他改进措施结合使用的特点。
The magnetic interference will destroy the attitude information contained in the magnetic sensor output, and thus reduce the attitude estimation accuracy of the fusion estimation algorithms based on magnetic, angular rate and gravity(MARG) measurements. In order to improve the anti-magnetic interference ability of attitude estimation, a method of correcting the magnetic sensor output under the condition of static or small motion acceleration is studied. In this method, the normalized magnetic sensor output vector is corrected by the gravity acceleration measurements. The correction method is based on the principle of a constant angle between the geomagnetic field and the gravity acceleration and the principle of minimum correction angle. The fusion model and the correction principle are analyzed, and the correction equations and formulas are deduced. The effectiveness of the correction method is verified by static and dynamic experiments. Experimental results show that the RMSE of pitch and roll estimation can be reduced by 2.6° and 1.6° respectively. In addition, the correction does not change the fusion process, and thus is compatible with other improvement measures.
The magnetic interference will destroy the attitude information contained in the magnetic sensor output, and thus reduce the attitude estimation accuracy of the fusion estimation algorithms based on magnetic, angular rate and gravity(MARG) measurements. In order to improve the anti-magnetic interference ability of attitude estimation, a method of correcting the magnetic sensor output under the condition of static or small motion acceleration is studied. In this method, the normalized magnetic sensor output vector is corrected by the gravity acceleration measurements. The correction method is based on the principle of a constant angle between the geomagnetic field and the gravity acceleration and the principle of minimum correction angle. The fusion model and the correction principle are analyzed, and the correction equations and formulas are deduced. The effectiveness of the correction method is verified by static and dynamic experiments. Experimental results show that the RMSE of pitch and roll estimation can be reduced by 2.6° and 1.6° respectively. In addition, the correction does not change the fusion process, and thus is compatible with other improvement measures.
引文
[1] AHMED H,TAHIR M.Accurate attitude estimation of a moving land vehicle using low-cost MEMS IMU sensors[J].IEEE Transactions on Intelligent Transportation Systems,2017,18(7):1723-1739.
[2] YEAN S,LEE B S,YEO C K,et al.Smartphone orientation estimation algorithm combining Kalman filter with gradient descent[J].IEEE Journal of Biomedical & Health Informatics,2018,22(5):1421-1433.
[3] XU X,TIAN X,ZHOU L.A robust incremental-quaternion-based angle and axis estimation algorithm of a single-axis rotation using MARG sensors[J].IEEE Access,2018,6(1):42605- 42615.
[4] WU J,ZHOU Z B,CHEN J J.Fast complementary filter for attitude estimation using low-cost MARG sensors[J].IEEE Sensors Journal,2016,16(8):6997-7007.
[5] MAZOMENOS E B,BISWAS D,CRANNY A,et al.Detecting elementary arm movements by tracking upper limb joint angles with MARG sensors[J].IEEE Journal of Biomedical & Health Informatics,2017,20(4):1088-1099.
[6] COSTANZI R,FANELLI F,MONNI N A.An attitude estimation algorithm for mobile robots under unknown magnetic disturbances[J].IEEE/ASME Transactions on Mechatronics,2016,21(4):1900-1911.
[7] TONG X,LI Z F,HAN,G W.Adaptive EKF based on HMM recognizer for attitude estimation using MEMS MARG sensors[J].IEEE Sensors Journal,2018,18(8):3299-3310.
[8] SUH Y S.Orientation estimation using a quaternion-based indirect Kalman filter with adaptive estimation of external acceleration[J].IEEE Transactions on Instrumentation and Measurement,2010,59(12):3296-3305.
[9] SHI G,LI X S,JIANG Z F.An improved yaw estimation algorithm for land vehicles using MARG sensors[J].Sensors,2018,18(10):3251-3268.
[10] YUN X,BACHMANN E R,MCGHEE R B.A simplified quaternion-based algorithm for orientation estimation from earth gravity and magnetic field measurements[J].IEEE Transactions on Instrumentation and Measurement,2008,57(3):638-650.
[11] VALENTI R G,DRYANOVSKI I,XIAO J.A linear Kalman filter for MARG orientation estimation using the algebraic quaternion algorithm[J].IEEE Transactions on Instrumentation and Measurement,2016,65(2):467- 481.
[12] FENG K Q,LI J,ZHANG X M,et al.A new quaternion-based Kalman Filter for real-time attitude estimation using the two-step geometrically-intuitive correction algorithm[J].Sensors,2017,17(9):2146- 2166.
[13] MICHAEL B,HEBA K,PHILLIP N,et al.Computationally efficient adaptive error-state Kalman filter for attitude estimation[J].IEEE Sensors Journal,2018,18(22):9332-9342.
[14] 陈孟元,谢义建,陈跃东.基于四元数改进型互补滤波的MEMS 姿态解算[J].电子测量与仪器学报,2015,29(9):1391-1397.CHEN M Y,XIE Y J,CHEN Y D.Attitude estimation of MEMS based on improved quaternion complementary filter[J].Journal of Electronic Measurement and Instrumentation,2015,29(9):1391-1397.
[15] 李婷,张金生,王仕成.基于阻尼粒子群优化的地磁场测量误差补偿[J].仪器仪表学报,2017,38(10):2446- 2452.LI T,ZHANG J SH,WANG SH CH.Compensation of geomagnetic field measurement error based on damped particle swarm optimization[J].Chinese Journal of Scientific Instrument,2017,38(10):2446- 2452.
[16] KOK M,SCHON T B.Magnetometer calibration using inertial sensors[J].IEEE Sensors Journal,2016,16(14):5679- 5689.
[2] YEAN S,LEE B S,YEO C K,et al.Smartphone orientation estimation algorithm combining Kalman filter with gradient descent[J].IEEE Journal of Biomedical & Health Informatics,2018,22(5):1421-1433.
[3] XU X,TIAN X,ZHOU L.A robust incremental-quaternion-based angle and axis estimation algorithm of a single-axis rotation using MARG sensors[J].IEEE Access,2018,6(1):42605- 42615.
[4] WU J,ZHOU Z B,CHEN J J.Fast complementary filter for attitude estimation using low-cost MARG sensors[J].IEEE Sensors Journal,2016,16(8):6997-7007.
[5] MAZOMENOS E B,BISWAS D,CRANNY A,et al.Detecting elementary arm movements by tracking upper limb joint angles with MARG sensors[J].IEEE Journal of Biomedical & Health Informatics,2017,20(4):1088-1099.
[6] COSTANZI R,FANELLI F,MONNI N A.An attitude estimation algorithm for mobile robots under unknown magnetic disturbances[J].IEEE/ASME Transactions on Mechatronics,2016,21(4):1900-1911.
[7] TONG X,LI Z F,HAN,G W.Adaptive EKF based on HMM recognizer for attitude estimation using MEMS MARG sensors[J].IEEE Sensors Journal,2018,18(8):3299-3310.
[8] SUH Y S.Orientation estimation using a quaternion-based indirect Kalman filter with adaptive estimation of external acceleration[J].IEEE Transactions on Instrumentation and Measurement,2010,59(12):3296-3305.
[9] SHI G,LI X S,JIANG Z F.An improved yaw estimation algorithm for land vehicles using MARG sensors[J].Sensors,2018,18(10):3251-3268.
[10] YUN X,BACHMANN E R,MCGHEE R B.A simplified quaternion-based algorithm for orientation estimation from earth gravity and magnetic field measurements[J].IEEE Transactions on Instrumentation and Measurement,2008,57(3):638-650.
[11] VALENTI R G,DRYANOVSKI I,XIAO J.A linear Kalman filter for MARG orientation estimation using the algebraic quaternion algorithm[J].IEEE Transactions on Instrumentation and Measurement,2016,65(2):467- 481.
[12] FENG K Q,LI J,ZHANG X M,et al.A new quaternion-based Kalman Filter for real-time attitude estimation using the two-step geometrically-intuitive correction algorithm[J].Sensors,2017,17(9):2146- 2166.
[13] MICHAEL B,HEBA K,PHILLIP N,et al.Computationally efficient adaptive error-state Kalman filter for attitude estimation[J].IEEE Sensors Journal,2018,18(22):9332-9342.
[14] 陈孟元,谢义建,陈跃东.基于四元数改进型互补滤波的MEMS 姿态解算[J].电子测量与仪器学报,2015,29(9):1391-1397.CHEN M Y,XIE Y J,CHEN Y D.Attitude estimation of MEMS based on improved quaternion complementary filter[J].Journal of Electronic Measurement and Instrumentation,2015,29(9):1391-1397.
[15] 李婷,张金生,王仕成.基于阻尼粒子群优化的地磁场测量误差补偿[J].仪器仪表学报,2017,38(10):2446- 2452.LI T,ZHANG J SH,WANG SH CH.Compensation of geomagnetic field measurement error based on damped particle swarm optimization[J].Chinese Journal of Scientific Instrument,2017,38(10):2446- 2452.
[16] KOK M,SCHON T B.Magnetometer calibration using inertial sensors[J].IEEE Sensors Journal,2016,16(14):5679- 5689.