Particle filtering with particle swarm optimization in systems with multiplicative noise

We propose a Particle Filter model that incorporates Particle Swarm Optimization for predicting systems with multiplicative noise. The proposed model employs a conventional multiobjective optimization approach to weight the likelihood and prior of the filter in order to alleviate the particle impoverishment problem. The resulting scheme is tested on a well–known test problem with multiplicative noise. Results are promising, especially in cases of high system and measurement noise levels. Categories and Subject Descriptors

• Type: Conference
• Year: 2008
• Where: GECCO
• Authors: A. D. Klamargias, Konstantinos E. Parsopoulos, Philipos D. Alevizos, Michael N. Vrahatis
• Keywords: GECCO 2008, Multiplicative Noise, Optimization, Particle Filter Model, Particle Swarm Optimization