Quaternion-Based Complementary Filter for Attitude Determination of a Smartphone
Quaternion-Based Complementary Filter for Attitude Determination of a Smartphone
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DOI：
10.1109/jsen.2016.2574124
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发表时间：
2016-08-01
影响因子：
4.3
通讯作者：
Redmond, Stephen J.
中科院分区：
文献类型：
--
作者：
Del Rosario, Michael B.;Lovell, Nigel H.;Redmond, Stephen J.
已经开发了一种几何直观的基于四元数的互补滤波器，该滤波器使用从设备的内部磁性和惯性测量单元获得的信号来估计智能手机的姿态。该方法引入了两个校正因子mu（α）和mu（m），其中mu（α）控制加速度计校正俯仰/滚转角的速率，mu（m）控制磁力计校正偏航角的速率。智能手机的姿态可以通过组合来自三轴陀螺仪（测量设备旋转的速率）、三轴加速度计（测量在没有身体移动的情况下由于重力引起的加速度）和三轴磁力计（可用于确定地磁北的方向）的信息来确定。为了验证该算法，将每个角落都贴有反光标记的非铁矩形板连接到智能手机上，使其姿态能够被光学运动捕捉系统跟踪。算法的准确度（phi（RMSE）= 3.37度，theta（RMSE）= 1.84度，psi（RMSE）= 4.83度）被评估为十个受试者进行五次正常的日常活动，同时智能手机被松散地固定到大腿上部以模拟被携带在裤子口袋中。还确定了算法在所有身体运动中的最佳调谐参数（mu（a）= 0.003和mu（m）= 0.001）。
A geometrically intuitive quaternion-based complementary filter has been developed, which estimates the attitude of a smartphone using signals obtained from the device's internal magnetic and inertial measurement unit. This method introduces two correction factors mu(a), which governs the rate at which the accelerometer corrects the pitch/roll angle, and mu(m), which controls the rate at which the yaw angle is corrected by the magnetometer. The smartphone's attitude can be determined by combining information from the tri-axial gyroscope (measures the rate at which the device rotates), the tri-axial accelerometer (measures the acceleration due to gravity in the absence of body movement), and the tri-axial magnetometer (can be used to determine the direction of geomagnetic north). In order to validate the algorithm, a nonferrous rectangular plate with retroreflective markers affixed at each corner was attached to the smartphone enabling its attitude to be tracked by an optical motion capture system. The accuracy of the algorithm (phi(RMSE) = 3.37 degrees, theta(RMSE) = 1.84 degrees, psi(RMSE) = 4.83 degrees) was assessed as ten subjects performed five normal daily activities while the smartphone was loosely fixed to the upper thigh to simulate being carried in a pants pocket. The optimal tuning parameters (mu(a) = 0.003 and mu(m) = 0.001) for the algorithm across all the body movements were also identified.