A parallel implementation of the sigma-point Kalman filter

Sigma-point Kalman filter (S-PKF) has shown promising performances when parameter identification and state tracking are simultaneously pursued in damaging structures. Unlike the extended Kalman Filter, the S-PKF continuously improves the outcomes (i.e. the estimates of state and model parameters) by averaging the responses of a set of independent sigma-points, which evolve in time according to the actual system dynamics. Being N the dimension of the state vector, sigma-points to deal with amount to 2N+1. Even though the S-PKF technique can become computationally demanding, its formulation can be exploited in a parallel implementation. Focusing on a parallelization scheme within a shared-memory (OPEN-MP) architecture, in this work scalability issues are discussed concerning real-time monitoring of damaging, large-scale composite structures.