论文标题
在线3轴磁力计硬铁和软铁偏置和角速度传感器偏置估计使用角速度传感器，以提高动态标题精度
Online 3-Axis Magnetometer Hard-Iron and Soft-Iron Bias and Angular Velocity Sensor Bias Estimation Using Angular Velocity Sensors for Improved Dynamic Heading Accuracy
论文作者
论文摘要
本文解决了在场上机器人技术中动态运动下的硬铁和软铁偏置的动态在线估计和软铁偏置的问题，仅利用3轴磁力计和3轴角速率传感器的偏置测量。提出的磁力计和角速度偏置估计量（MAVBE）使用了15个状态的过程模型，该模型编码了磁力计信号的非线性过程动力学，但受角速度偏移的影响，而同时估计9磁力计偏置偏置参数和3个角度速率传感器偏置参数，并在Exterman pilter pertrender pertrender pertrender pertrender prilter oferned prester prilter oferned prester exprended exprended exprended exprended exprended exprended exprender exprended exprender exprended框架内。偏差参数局部可观察性是数值评估的。偏置补偿的信号以及3轴加速度计信号可用于估计偏置补偿的磁性大地测量线。与广泛引用的磁力计在数值模拟，实验室实验和美国马里兰州切萨皮克湾的仪器自动驾驶水下车辆的全面现场试验相比，评估了所提出的MAVBE方法的性能。 For the proposed MAVBE, (i) instrument attitude is not required to estimate biases, and the results show that (ii) the biases are locally observable, (iii) the bias estimates converge rapidly to true bias parameters, (iv) only modest instrument excitation is required for bias estimate convergence, and (v) compensation for magnetometer hard-iron and soft-iron biases dramatically improves dynamic heading estimation 准确性。
This article addresses the problem of dynamic on-line estimation and compensation of hard-iron and soft-iron biases of 3-axis magnetometers under dynamic motion in field robotics, utilizing only biased measurements from a 3-axis magnetometer and a 3-axis angular rate sensor. The proposed magnetometer and angular velocity bias estimator (MAVBE) utilizes a 15-state process model encoding the nonlinear process dynamics for the magnetometer signal subject to angular velocity excursions, while simultaneously estimating 9 magnetometer bias parameters and 3 angular rate sensor bias parameters, within an extended Kalman filter framework. Bias parameter local observability is numerically evaluated. The bias-compensated signals, together with 3-axis accelerometer signals, are utilized to estimate bias compensated magnetic geodetic heading. Performance of the proposed MAVBE method is evaluated in comparison to the widely cited magnetometer-only TWOSTEP method in numerical simulations, laboratory experiments, and full-scale field trials of an instrumented autonomous underwater vehicle in the Chesapeake Bay, MD, USA. For the proposed MAVBE, (i) instrument attitude is not required to estimate biases, and the results show that (ii) the biases are locally observable, (iii) the bias estimates converge rapidly to true bias parameters, (iv) only modest instrument excitation is required for bias estimate convergence, and (v) compensation for magnetometer hard-iron and soft-iron biases dramatically improves dynamic heading estimation accuracy.
