Parallelized sigma-point Kalman filtering for structural dynamics

In this paper, the sigma-point Kalman filter (S-PKF) is adopted to track the state of composite structures undergoing impact-induced delamination. Estimates provided by the S-PKF are obtained through a set of sigma-points, which independently evolve in time according to the system dynamics. Since the number of sigma-points grows proportionally to the number of degrees of freedom of the space-discretized structural system, the S-PKF can become computationally demanding. Starting from the aforementioned independent evolution of the sigma-points, we propose a parallel implementation of the S-PKF within a shared-memory (OpenMP) architecture. Scalability and accuracy issues are eventually discussed.

► Sigma-point Kalman filter is adopted to track the state of composite structures undergoing impact-induced delamination. ► Starting from the independent evolution of sigma-points, we propose a parallel implementation of the filter. ► A shared-memory (OpenMP) architecture is adopted.