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Efficient recursive state estimator for dynamic systems without knowledge of noise covariances

机译：动态系统的高效递归状态估计器，无需了解噪声协方差

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An efficient recursive state estimator for dynamic systems without knowledge of noise covariances is suggested. The basic idea for this estimator is to incorporate the dynamic matrix and the forgetting factor into the least squares (LS) method to remedy the lack of knowledge of noises. We call it the extended forgetting factor recursive least squares (EFRLS) estimator. This estimator is shown to have similar asymptotic properties to a completely specified Kalman filter state estimator. More importantly, the performance of EFRLS greatly exceeds that of existing filtering techniques when the noise variance is misspecified. In addition, EFRLS also performs well when there is cross-correlation between the process and measurement noise streams or temporal dependencies within those streams. Some discussions and a number of simulations are made to provide practical guidance on the choice of an optimal forgetting factor and evaluate the performance of the EFRLS algorithms, which strongly dominates that of the standard forgetting factor recursive least squares (FRLS) and some misspecified Kalman filtering.
机译：提出了一种动态系统的有效递归状态估计器，该估计器无需了解噪声协方差。该估计器的基本思想是将动态矩阵和遗忘因子合并到最小二乘（LS）方法中，以弥补对噪声知识的缺乏。我们称其为扩展遗忘因子递归最小二乘（EFRLS）估计器。该估计器显示为与完全指定的卡尔曼滤波器状态估计器具有相似的渐近性质。更重要的是，如果错误指定了噪声方差，则EFRLS的性能将大大超过现有的滤波技术。此外，当过程噪声流与测量噪声流或这些流中的时间相关性之间存在互相关性时，EFRLS也会表现良好。进行了一些讨论和大量模拟，以为选择最佳遗忘因子提供实际指导，并评估EFRLS算法的性能，该算法强烈地控制了标准遗忘因子递归最小二乘（FRLS）和某些错误指定的卡尔曼滤波。

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