Oct 12, 2022 · In this work, an outlier-insensitive KF is proposed, where robustness is achieved by modeling each potential outlier as a normally distributed random variable ...

In this work, an outlier-insensitive KF (OIKF) is proposed, where robustness is achieved by modeling a potential outlier as a normally distributed random ...

The empirical study demonstrates that the MSE of the proposed outlier-insensitive KF outperforms previously proposed algorithms, and that for data clean of ...

Feb 14, 2024 · The nuv formulation models a variable of interest as a normal distribution with unknown variance, given that the unknown variance has a prior ...

In this work, an outlier-insensitive KF (OIKF) is proposed, where robustness is achieved by modeling a potential outlier as a normally distributed random ...

This effect likely follows from the similar behavior exhibited by the KF, which increased its MSE 100× with the same noise magnitude.

Fingerprint. Dive into the research topics of 'Outlier-Insensitive Kalman Filtering Using NUV Priors'. Together they form a unique fingerprint.

In this paper we propose a new approach to outlier- insensitive Kalman smoothing (NUV-EM OIKS): using an idea from sparse Bayesian learning [6], we model ...

We propose a new approach to outlier-insensitive Kalman smoothing based on normal priors with unknown variance (NUV).

Nov 28, 2024 · To mitigate such behavior, outlier detection algorithms can be applied. In this work, we propose a parameter-free algorithm which mitigates the ...