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首页> 外文期刊>Aerospace and Electronic Systems, IEEE Transactions on >Direction Cosine Matrix Estimation from Vector Observations using a Matrix Kalman Filter
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Direction Cosine Matrix Estimation from Vector Observations using a Matrix Kalman Filter

机译：使用矩阵卡尔曼滤波器的矢量观测值的方向余弦矩阵估计

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摘要

This work presents several algorithms that use vector observations in order to estimate the direction cosine matrix (DCM) as well as three constant biases and three time-varying drifts in body-mounted gyro output errors. All the algorithms use the matrix Kalman filter (MKF) paradigm, which preserves the natural formulation of the DCM state-space model equations. Focusing on the DCM estimation problem, the assumption of white noise in the gyro and in the vector observations errors yields reduced and efficient filter covariance computations. The orthogonality constraint on the DCM is handled via the technique of pseudomeasurement, which is naturally embedded in the MKF. Two additional known "brute-force" procedures are implemented for the sake of comparison. Extensive Monte-Carlo simulations illustrate the performances of the different estimators. When estimating only the DCM, it is shown that all the proposed orthogonalization procedures accelerate the estimation convergence. Nevertheless, the pseudomeasurement technique shows a smoother and shorter transient than the brute-force procedures, which on the other hand yield more accurate steady-states. The reduced covariance computations yield a more accurate steady-state than the full covariance computations but show a slower transient. When estimating the DCM as well as the gyro biases and drifts, enforcing orthogonalization seems to penalize the DCM estimation as long as the biases are not correctly identified. For the sake of computation savings during long duration missions, a mixed estimator, switching between long periods of DCM-only estimation and short periods of DCM-biases estimation, appears to be a promising strategy.
机译：这项工作提出了几种使用矢量观测的算法，以估计方向余弦矩阵（DCM）以及人体上陀螺仪输出误差中的三个恒定偏差和三个时变漂移。所有算法均使用矩阵卡尔曼滤波器（MKF）范例，该范例保留了DCM状态空间模型方程式的自然形式。着重于DCM估计问题，在陀螺仪和矢量观测误差中假设白噪声会产生减少且有效的滤波器协方差计算。 DCM的正交性约束是通过伪测量技术处理的，该技术自然嵌入在MKF中。为了比较，实施了另外两个已知的“强力”程序。广泛的蒙特卡洛模拟说明了不同估计量的性能。当仅估计DCM时，表明所有建议的正交化过程都加速了估计收敛。然而，伪测量技术显示出比蛮力过程更平滑和更短的瞬态，另一方面，它产生更准确的稳态。降低的协方差计算产生的稳态比完全协方差计算更精确，但瞬变更慢。在估算DCM以及陀螺仪的偏差和漂移时，只要未正确识别偏差，强制执行正交化似乎会对DCM估算产生不利影响。为了节省长时间任务期间的计算量，在仅DCM估计的长周期和DCM偏差估计的短周期之间进行切换的混合估计器似乎是一种有前途的策略。

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