Higher-Degree Stochastic Integration Filtering

2016/08/01 by Syed Safwan Khalid, Khalid, Syed Safwan, Naveed ur Rehman +3
Computer Science · Engineering · #FOS: Computer and information sciences #FOS: Electrical engineering #Inertial Sensor and Navigation #Robotics (cs.RO) #Structural Health Monitoring Techniques #Systems and Control (eess.SY) #Target Tracking and Data Fusion in Sensor Networks #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.1608.00337

openalex publication_date 2016/08/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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.

Citations

• A new method for the nonlinear transformation of means and covariances in filters and estimators

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