Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
5003080 | IFAC-PapersOnLine | 2016 | 6 Pages |
Abstract
This paper addresses the design of a new set-membership particle filter named Box Regularized Particle Filter (BRPF) applied to terrain navigation. This algorithm combines the set-membership particle estimation (known as Box Particle Filter) with the Kernel estimation method. This approach makes possible to enhance significantly the filter's robustness while reducing the computation time (only 200 particles are needed instead of 5,000 with a conventional Sequential Importance Resampling (SIR) Particle Filter). Numerical results are presented from 10,000 Monte-Carlo runs.
Related Topics
Physical Sciences and Engineering
Engineering
Computational Mechanics
Authors
Nicolas Merlinge, Karim Dahia, Hélène Piet-Lahanier,