We consider the robust parameter estimation problem for state-space models with correlated measurements in the presence of outliers. Both the scenarios that the statistical information of the nominal noises in state-space models is known or not are explored. A novel Kalman robust smoother is proposed via introducing a specific reweighting approach to estimate the system parameters as well as the states when the nominal noise covariances are known. For the case where the statistic information of the nominal noise is absent, a modified expectation–maximization algorithm is introduced and integrated into the proposed robust smoother for simultaneously estimating the unknown states, system parameters, and the noise covariances. The performance of the proposed methods is illustrated by solving the aerodynamic coefficient identification problem with the real flight data set. The simulation results reveal that the proposed approaches outperform several existing solutions when outliers occur in multiple components of correlated measurements.

我们考虑在存在异常值的情况下具有相关测量值的状态空间模型的稳健参数估计问题。探索了状态空间模型中标称噪声的统计信息已知或未知的两种情况。当标称噪声协方差已知时，通过引入特定的重新加权方法来估计系统参数以及状态，提出了一种新颖的卡尔曼鲁棒平滑器。对于没有标称噪声统计信息的情况，引入了改进的期望最大化算法并将其集成到所提出的鲁棒平滑器中，用于同时估计未知状态、系统参数和噪声协方差。通过使用真实飞行数据集解决气动系数识别问题，说明了所提出方法的性能。仿真结果表明，当相关测量的多个分量中出现异常值时，所提出的方法优于几种现有的解决方案。