OUTLIER-INSENSITIVE KALMAN FILTERING USING NUV PRIORS
Shunit Truzman (University of Haifa); Guy Revach (ETH Zürich); Nir Shlezinger (Ben-Gurion University); Itzik Klein (University of Haifa)
-
SPS
IEEE Members: $11.00
Non-members: $15.00
The Kalman filter (KF) is a widely-used algorithm for tracking
the latent state of a dynamical system from noisy observations.
For systems that are well-described by linear Gaussian
state space models, the KF minimizes the mean-squared error
(MSE). However, in practice, observations are corrupted
by outliers, severely impairing the KF’s performance. In this
work, an outlier-insensitive KF (OIKF) is proposed, where
robustness is achieved by modeling a potential outlier as a
normally distributed random variable with unknown variance
(NUV). The NUV’s variance is estimated online, using
both expectation-maximization (EM) and alternating maximization
(AM). The former was previously proposed for the
task of smoothing with outliers and was adapted here to filtering,
while both EM and AM obtained the same performance
and outperformed the other algorithms, the AM approach
is less complex and thus requires 40% less runtime. Our
empirical study demonstrates that the MSE of our proposed
outlier-insensitive KF outperforms previously proposed algorithms,
and that for data clean of outliers, it reverts to the
classic KF, i.e., MSE optimality is preserved.