Authors: Samuel Nyobe ^1,2; Fabien Campillo ³; Serge Moto ^1,2; Vivien Rossi ^4,5,6

• 1 University of Yaoundé [Cameroun]
• 2 Unité de modélisation mathématique et informatique des systèmes complexes [Bondy]
• 3 Mathématiques pour les Neurosciences
• 4 Forêts et Sociétés
• 5 University of Yaounde I, National Advanced School of Engineering, Department of Mathematic and Physical Science
• 6 National Advanced School of Engineering

Sequential Monte Carlo methods have been a major breakthrough in the field of numerical signal processing for stochastic dynamical state-space systems with partial and noisy observations. However, these methods still present certain weaknesses. One of the most fundamental is the degeneracy of the filter due to the impoverishment of the particles: the prediction step allows the particles to explore the state-space and can lead to the impoverishment of the particles if this exploration is poorly conducted or when it conflicts with the following observation that will be used in the evaluation of the likelihood of each particle. In this article, in order to improve this last step within the framework of the classic bootstrap particle filter, we propose a simple approximation of the one step fixed- lag smoother. At each time iteration, we propose to perform additional simulations during the prediction step in order to improve the likelihood of the selected particles.