Sigma-point multiple particle filtering

• Published in: Signal processing Vol. 160; pp. 271 - 283
• Main Authors: Úbeda-Medina, Luis, García-Fernández, Ángel F., Grajal, Jesús
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01-07-2019
• Subjects: Curse of dimensionality; Multiple particle filter; Particle filters; Sigma-point; Unscented transform; Curse of dimensionality; Particle filters; Sigma-point; Unscented transform; Multiple particle filter
• ISSN: 0165-1684; 1872-7557
• DOI: 10.1016/j.sigpro.2019.02.019

•This paper contributes with two new algorithms within the multiple particle filtering framework that provide an accurate and efficient tool to tackle the challenging problem of filtering in high dimensional state spaces. The proposed algorithms significantly outperform the state-of-the-art in particle filtering for high-dimensional state spaces with low computational burden.•The proposed methods make use of a novel combination of sigma-point integration methods and multiple particle filtering to provide a second order approximation to the marginalization integrals required in the multiple filtering field.•Our approach is not restricted for its use with a specific measurement model and does not require analytic derivations. In addition, the computational cost of the presented algorithms is not increased with respect to similar methods. The resulting particle filters outperform the state-ofthe-art particle filters in high-dimensional problems.
In this paper, we introduce two new particle filtering algorithms for high-dimensional state spaces in the multiple particle filtering approach. In multiple particle filtering, the state space is partitioned and a different particle filter is used for each component of the partition. At each time step, all particle filters share information about their marginal densities so that they can adequately approximate the filtering recursion. In this paper, we propose a second order approximation to the involved densities based on sigma-point integration methods. We then introduce two different particle filters that make use of this strategy. Finally, we demonstrate their remarkable performance through simulations of a multiple target tracking scenario with a sensor network.