Application of Particle Filtering for Estimating the Dynamics of Nuclear Systems

System dynamics estimation is a crucial issue for the safe operation and control of nuclear power plants. Typically, the estimation is based on a model of the plant dynamics and related measurements. In practice, the non-linearity of the dynamics and non-Gaussianity of the noise associated to the process and measurements lead to inaccurate results even with advanced approaches, such as the Kalman, Gaussian-sum and grid-based filters. On the contrary, accurate results may be obtained with Monte Carlo-based estimation methods, also called particle filters. The present paper illustrates the developments of a previous work by the same authors with regards to the comparison of the so called Sampling Importance Resampling filter method with the standard and extended Kalman filtering techniques. Two case studies are analyzed to separately highlight the effect of non-linearity and non-Gaussianity in the process noise.