An improved particle filtering for time-varying nonlinear prediction of biomass and grain protein content

The simulation results of both comparative studies show that the IPF provides a significant improvement over the PF. This is because, unlike the PF which depends on the choice of sampling distribution used to estimate the posterior distribution, the IPF yields an optimum choice of this distribution, which also utilizes the observed data. The results of the comparative studies show also that, for all the techniques, estimating more model parameters affects the estimation accuracy as well as the convergence of the estimated states and parameters. The performance of the proposed method is evaluated on a synthetic example in terms of estimation accuracy, root mean square error and execution times.