  State estimation of dynamical systems is crucial for providing new
decision-making and system automation information in different applications.
However, the assumptions on the standard computational models for sensor
measurements can be violated in practice due to different types of data
abnormalities such as outliers and biases. In this work, we focus on the
occurrence of measurement biases and propose a robust filter for their
detection and mitigation during state estimation of nonlinear dynamical
systems. We model the presence of bias in each dimension within the generative
structure of the state-space models. Subsequently, employing the theory of
Variational Bayes and general Gaussian filtering, we devise a recursive filter
which we call the Bias Detecting and Mitigating (BDM) filter. As the error
detection mechanism is embedded within the filter structure its dependence on
any external detector is obviated. Simulations verify the performance gains of
the proposed BDM filter compared to similar Kalman filtering-based approaches
in terms of robustness to temporary and persistent bias presence.
