文献类型：期刊文献

英文题名：Outlier-Robust Autocovariance Least-Squares Estimation via Iteratively Reweighted Least Squares

作者：Li, Jiahong[1];Deng, Fang[2,3]

第一作者：李佳洪

通讯作者：Deng, F[1];Deng, F[2]

机构：[1]Beijing Union Univ, Coll Robot, Beijing 100101, Peoples R China;[2]Beijing Inst Technol, State Key Lab Autonomous Intelligent Unmanned Syst, Beijing 100081, Peoples R China;[3]Beijing Inst Technol, Sch AI, Beijing 100081, Peoples R China

第一机构：北京联合大学机器人学院

通讯机构：[1]corresponding author), Beijing Inst Technol, State Key Lab Autonomous Intelligent Unmanned Syst, Beijing 100081, Peoples R China;[2]corresponding author), Beijing Inst Technol, Sch AI, Beijing 100081, Peoples R China.

年份：2026

卷号：33

起止页码：2415-2419

外文期刊名：IEEE SIGNAL PROCESSING LETTERS

收录：;Scopus(收录号：2-s2.0-105040922341);WOS:【SCI-EXPANDED(收录号:WOS:001795849100012)】；

基金：This work was supported in part by the R&D Program of Beijing Municipal Education Commission under Grant KM202411417006, in part by the National Natural Science Foundation of China under Grant 92367109 and Grant 61973035, and in part by Beijing Union University High-level Fund Incubation Project under Grant ZK20202509.

语种：英文

外文关键词：Aluminum; Kalman filters; Noise; Estimation; Filters; Filtering; Technological innovation; Measurement; Equations; Modeling; Autocovariance least squares; covariance identification; iteratively reweighted least squares; Kalman filter; robust estimation

摘要：Accurate process and measurement noise covariances are indispensable for Kalman filtering, yet they are difficult to identify when the innovation used for calibration is intermittently corrupted by sensor outliers. Conventional autocovariance least-squares (ALS) estimates the noise covariances by matching empirical and theoretical innovation autocovariances, but its quadratic criterion can convert a few impulsive autocovariance entries into large covariance bias. This paper develops an outlier-robust ALS estimator, ALS-IRLS, for covariance identification under such contamination. ALS-IRLS casts ALS as a structured Huber $M$-estimation problem on the stacked multi-lag autocovariance equations and solves the induced weighted least-squares subproblems by iteratively reweighted least squares (IRLS) within the Riccati/gain fixed-point loop. This design preserves the information that separates process and measurement noise while bounding the influence of contaminated equations. Bounded-influence, threshold-sensitivity, local-convergence, admissibility, and amortized-complexity analyses are provided. Simulations under 15% contamination show that ALS-IRLS reduces covariance-estimation errors by over two orders of magnitude relative to ALS and yields downstream filtering accuracy close to the oracle Kalman filter, including against adaptive robust-filter baselines.