Particle filtering without tears: A primer for beginners

• Published in: Computers & chemical engineering 2016-12, Vol.95, p.130-145
• Main Authors: Tulsyan, Aditya, Bhushan Gopaluni, R., Khare, Swanand R.
• Format: Article
• Language: English
• Subjects: Bayesian inference; Monte Carlo method; Particle filter; State estimation
• ISSN: 0098-1354; 1873-4375
• DOI: 10.1016/j.compchemeng.2016.08.015

•A primer to systematically introduce the theory of particle filters to the reader.•For beginners interested in the theory and implementation of particle filters.•Presents the application of particle filters for the state estimation problem.•Provides an implementable MATLAB code for state estimation using particle filters. The main purpose of this primer is to systematically introduce the theory of particle filters to readers with limited or no prior understanding of the subject. The primer is written for beginners and practitioners interested in learning about the theory and implementation of particle filtering methods. Throughout this primer we highlight the common mistakes that beginners and first-time researchers make in understanding and implementing the theory of particle filtering. We also discuss and demonstrate the use of particle filtering in nonlinear state estimation applications. We conclude the primer by providing an implementable version of MATLAB code for particle filters. The code not only aids in improving the understanding of particle filters, it also serves as a template for building and implementing advanced nonlinear state estimation routines.