Outlier-robust Autocovariance Least Square Estimation via Iteratively Reweighted Least Square
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 empl...
| Authors | Jiahong Li, Fang Deng |
|---|---|
| Primary category | math.OC |
| Published | 9 March 2026 |
| Last updated at source | 9 March 2026 |
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