Noise Identification for Data-Augmented Physics-Based State-Space Models

This paper deals with the state-space modelling of nonlinear stochastic dynamic systems. The emphasis is laid on the emerging area of data-augmented physics-based modelling of the state dynamics, which combines the benefits of the physics-driven and data-based identified models. As the augmented state-space models depend on the measured data, modelling the state noise properties becomes challenging. This paper proposes and validates a concept for the state noise identification of nonlinear data-augmented state equation using the maximum likelihood and correlation-based methods. The numerical simulation of a tracking scenario shows significant improvement of the state estimation accuracy and consistency when using the identified noise model.