Bias-Aware Bayesian Filtering Using Gaussian Process Latent Force Models

Abstract

An important consideration when using sequential Bayesian filters for the estimation of unknown states and inputs, is the potential impact of modelling errors on the accuracy and precision of the estimation. When the modelling errors are explicitly accounted for in the estimation, one can speak of bias-aware filtering. One approach that has been suggested to achieve bias-aware filtering is the use of Gaussian process latent force models. Using this approach, the biases are represented as Gaussian processes and identified from the vibration data in conjunction with the additional unknown quantities. In this contribution, we focus on modelling biases due to miscalibrated dynamic properties in linear system models. We analyze the treatment of such biases using Gaussian process latent force models, and explore the robustness of the approach to changes in sensor configurations using simulated data from a cantilever beam.