文献类型：期刊文献

英文题名：Outlier-robust Autocovariance Least Square Estimation via Iteratively Reweighted Least Square

作者：Li, Jiahong[1]; Deng, Fang[2]

第一作者：李佳洪

机构：[1] College of Robotics, Beijing Union University, Beijing, 100101, China; [2] Key Laboratory of Intelligent Control and Decision of Complex Systems, School of Automation, Beijing Institute of Technology, Beijing, 100081, China

第一机构：北京联合大学机器人学院

年份：2026

外文期刊名：arXiv

收录：EI(收录号：20260144884)

语种：英文

外文关键词：Computational efficiency - Iterative methods - Least squares approximations - Mean square error - Robustness (control systems) - State estimation - Statistics

摘要：The autocovariance least squares (ALS) method is a computationally efficient approach for estimating noise covariances in Kalman filters without requiring specific noise models. However, conventional ALS and its variants rely on the classic least mean squares (LMS) criterion, making them highly sensitive to measurement outliers and prone to severe performance degradation. To overcome this limitation, this paper proposes a novel outlier-robust ALS algorithm, termed ALS-IRLS, based on the iteratively reweighted least squares (IRLS) framework. Specifically, the proposed approach introduces a two-tier robustification strategy. First, an innovation-level adaptive thresholding mechanism is employed to filter out heavily contaminated data. Second, the outlier-contaminated autocovariance is formulated using an ?-contamination model, where the standard LMS criterion is replaced by the Huber cost function. The IRLS method is then utilized to iteratively adjust data weights based on estimation deviations, effectively mitigating the influence of residual outliers. Comparative simulations demonstrate that ALS-IRLS reduces the root-mean-square error (RMSE) of noise covariance estimates by over two orders of magnitude compared to standard ALS. Furthermore, it significantly enhances downstream state estimation accuracy, outperforming existing outlier-robust Kalman filters and achieving performance nearly equivalent to the ideal Oracle lower bound in the presence of noisy and anomalous data. ? 2026, CC BY.