Cubature Information Filters Using High-Degree and Embedded Cubature Rules

Abstract

The information form of the Kalman filter (KF) is preferred over standard covariance filters in multiple sensor fusion problems. Aiming at this issue, two types of cubature information filters (CIF) for nonlinear systems are presented in this article. The two approaches, which we have named the embedded cubature information filter (ECIF) and the fifth-degree cubature information filter (FCIF), are developed from a fifth-degree cubature Kalman filter and a newly proposed embedded cubature KF. Theoretical analysis shows that the proposed filters can achieve higher level estimation accuracy than conventional information filters, such as the CIF and the extended information filter (EIF). Performance comparisons of the proposed information filters with the conventional CIF are demonstrated via two independent multisensor tracking problems. The experimental results, presented herein, demonstrate that the proposed algorithms are more reliable and accurate than the CIF.