Ensemble Particle Filter Based on KLD and Its Application to Initial Alignment of the SINS in Large Misalignment Angles

Abstract

Among many auxiliary particle filtering algorithms, the ensemble particle filter (EnPF) has been widely applied in various fields, which can be ascribed to its avoidance of the heavy computational burden resulted by the parallel computation of multifilters. However, the estimation precision of the EnPF, like other auxiliary particle filters, also decreases when the nonlinear extent of a system aggravates. In order to have the advantages of the EnPF promoted into strongly nonlinear systems, this paper first analyzes the reasons why the EnPF deteriorates in strongly nonlinear systems and then proposes an EnPF based on Kullback–Leibler distance (KLD), which is named KLEnPF. KLEnPF is mainly inspired by the Gaussian sum approximation. For the aim of improving the approximation precision, the KLEnPF calculates the optimal bandwidth parameters of Gaussian kernels to minimize the KLD between two distribution functions. Finally, we apply the KLEnPF to the initial alignment of the strapdown inertial navigation system (SINS) in large misalignment angles, which is a strongly nonlinear system. The experimental results of it demonstrate the effectiveness of the KLEnPF.