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A new robust quaternion-based initial alignment algorithm for stationary strapdown inertial navigation systems
Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering ( IF 1.1 ) Pub Date : 2020-04-27 , DOI: 10.1177/0954410020920473 Habib Ghanbarpourasl 1
Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering ( IF 1.1 ) Pub Date : 2020-04-27 , DOI: 10.1177/0954410020920473 Habib Ghanbarpourasl 1
Affiliation
A new robust quaternion Kalman filter is developed for accurate alignment of stationary strapdown inertial navigation system. Most fine alignment algorithms have tried to estimate the biases of gyroscopes and accelerometers to reduce the errors of the alignment process. In stationary platforms, due to fixed inputs for sensors, the summation of various errors such as fixed bias, misalignment, scale factor, and nonlinear errors acts like one bias error, and then the identification of each error will be impossible. The observability of gyros and accelerometers’ biases has also been studied. But, nowadays, we know that all of these unknown parameters are not observable. Then this problem can increase the complication of the alignment algorithm. The accelerometers’ errors mainly affect the errors of the roll and pitch angles, but a big portion of the heading’s error results from the gyroscopes’ errors. Modeling of all errors as additional states without considering the observability parameters has no benefits, but will increase the filter’s dimension, so the filter’s performance will decrease. In this study, due to the observability problem, a new robust multiplicative quaternion Kalman filter is designed for the alignment of a stationary platform. The presented algorithm does not estimate the sensors’ errors, but it is robust to uncertainty in the sensors’ errors. In the proposed scheme, the bounds of parameters’ errors are introduced to filter, and the filter tries to remain robust with respect to these uncertainties. The method uses the benefits of quaternions in attitude modeling, and then the robust filter is adapted to work with quaternions. The ability of the new algorithm is evaluated with MATLAB simulations. The outcomes show that the presented algorithm is more accurate than other traditional methods. The extended Kalman filter with accelerometers’ outputs and the horizontal velocities as the measurement equations and additive quaternion Kalman filter are used for comparisons.
中文翻译:
一种新的基于四元数的固定捷联惯性导航系统初始对齐算法
开发了一种新的强大的四元数卡尔曼滤波器,用于固定捷联惯性导航系统的精确对准。大多数精细对准算法都试图估计陀螺仪和加速度计的偏差,以减少对准过程的误差。在固定平台中,由于传感器的输入是固定的,固定偏差、错位、比例因子和非线性误差等各种误差的总和就像一个偏差误差,然后就不可能识别每个误差。还研究了陀螺仪和加速度计偏差的可观察性。但是,如今,我们知道所有这些未知参数都是不可观察的。那么这个问题会增加对齐算法的复杂度。加速度计的误差主要影响滚转角和俯仰角的误差,但航向误差的很大一部分是由陀螺仪的误差造成的。将所有错误建模为附加状态而不考虑可观察性参数没有任何好处,但会增加过滤器的维度,因此过滤器的性能会下降。在这项研究中,由于可观察性问题,设计了一种新的鲁棒乘法四元数卡尔曼滤波器,用于固定平台的对齐。所提出的算法不估计传感器的误差,但它对传感器误差的不确定性具有鲁棒性。在所提出的方案中,参数误差的界限被引入到过滤器中,并且过滤器试图保持对这些不确定性的鲁棒性。该方法利用四元数在姿态建模中的优势,然后将鲁棒滤波器适应于与四元数一起工作。新算法的能力通过 MATLAB 仿真进行评估。结果表明,所提出的算法比其他传统方法更准确。使用加速度计输出和水平速度作为测量方程的扩展卡尔曼滤波器和加性四元数卡尔曼滤波器进行比较。
更新日期:2020-04-27
中文翻译:
一种新的基于四元数的固定捷联惯性导航系统初始对齐算法
开发了一种新的强大的四元数卡尔曼滤波器,用于固定捷联惯性导航系统的精确对准。大多数精细对准算法都试图估计陀螺仪和加速度计的偏差,以减少对准过程的误差。在固定平台中,由于传感器的输入是固定的,固定偏差、错位、比例因子和非线性误差等各种误差的总和就像一个偏差误差,然后就不可能识别每个误差。还研究了陀螺仪和加速度计偏差的可观察性。但是,如今,我们知道所有这些未知参数都是不可观察的。那么这个问题会增加对齐算法的复杂度。加速度计的误差主要影响滚转角和俯仰角的误差,但航向误差的很大一部分是由陀螺仪的误差造成的。将所有错误建模为附加状态而不考虑可观察性参数没有任何好处,但会增加过滤器的维度,因此过滤器的性能会下降。在这项研究中,由于可观察性问题,设计了一种新的鲁棒乘法四元数卡尔曼滤波器,用于固定平台的对齐。所提出的算法不估计传感器的误差,但它对传感器误差的不确定性具有鲁棒性。在所提出的方案中,参数误差的界限被引入到过滤器中,并且过滤器试图保持对这些不确定性的鲁棒性。该方法利用四元数在姿态建模中的优势,然后将鲁棒滤波器适应于与四元数一起工作。新算法的能力通过 MATLAB 仿真进行评估。结果表明,所提出的算法比其他传统方法更准确。使用加速度计输出和水平速度作为测量方程的扩展卡尔曼滤波器和加性四元数卡尔曼滤波器进行比较。