Repozytorium publikacji - Politechnika Gdańska

While local basis function (LBF) estimation algorithms, commonly used for identifying/tracking systems with time-varying parameters, demonstrate good performance under the assumption of normally distributed measurement noise, the estimation results may significantly deviate from satisfactory when the noise distribution is of impulsive nature, for example, heavy-tailed or corrupted by outliers. This paper introduces a computationally efficient method to make LBF estimator robust, enhancing its resistance to impulsive noise. The study illustrates that, for polynomial basis functions, this modified LBF estimator can be computed recursively. Furthermore, it demonstrates that the proposed algorithm can undergo online tuning through parallel estimation and leave-one-out crossvalidation.

Autorzy

• prof. dr hab. inż. Maciej Niedźwiecki link otwiera się w nowej karcie ,
• dr inż. Artur Gańcza link otwiera się w nowej karcie ,
• Wojciech Żuławiński,
• Agnieszka Wyłomańska