Higher-Degree Stochastic Integration Filtering
Systems and Control
2016-08-02 v1 Robotics
Abstract
We obtain a class of higher-degree stochastic integration filters (SIF) for nonlinear filtering applications. SIF are based on stochastic spherical-radial integration rules that achieve asymptotically exact evaluations of Gaussian weighted multivariate integrals found in nonlinear Bayesian filtering. The superiority of the proposed scheme is demonstrated by comparing the performance of the proposed fifth-degree SIF against a number of existing stochastic, quasi-stochastic and cubature (Kalman) filters. The proposed filter is demonstrated to outperform existing filters in all cases.
Cite
@article{arxiv.1608.00337,
title = {Higher-Degree Stochastic Integration Filtering},
author = {Syed Safwan Khalid and Naveed Ur Rehman and Shafayat Abrar},
journal= {arXiv preprint arXiv:1608.00337},
year = {2016}
}
Comments
Submitted to IEEE Signal Processing Letters on 17th July 2016