Transmissibility-based Kalman Filtering For Systems With Non-Gaussian Process Noise

Abstract

The concept of transmissibility operators refers to the mathematical relationships between system outputs. They can be used to estimate the independent output of a system based on sensor measurements only. In this case, the output estimation is independent of the process noise or unmodeled dynamics. This allows for the estimation of process noise regardless of its probability distribution. The proposed approach takes into account the possibility of using the Kalman filter theme in the filtering of output noise regardless of the process noise distribution. The proposed approach does not require the covariance estimation of the process noise. Since the proposed approach considers the ability to formulate unmodeled dynamics or parameter uncertainties as non-Gaussian process noise, it can handle both. The potential of this approach is demonstrated by implementing it in a group of connected autonomous robots.