An Improved High-Degree Cubature Particle Filter and its Application in Bearing-only Tracking

Abstract

In this study, a fifth-degree cubature particle filter (5CPF) is proposed to address the limited estimation accuracy in traditional particle filter algorithms for bearings-only tracking (BOT). This algorithm calculates the recommended density function by introducing a fifth-degree cubature Kalman filter algorithm to guide particle sampling, which effectively alleviates the problem of particle degradation and significantly improves the estimation accuracy of the filter. However, the 5CPF algorithm exhibits high computational complexity, particularly in scenarios with a large number of particles. Therefore, we propose the extended Kalman filter (EKF)-5CPF algorithm, which employs an EKF to replace the time update step for each particle in the 5CPF. This enhances the algorithm’s real-time capability while maintaining the high precision advantage of the 5CPF algorithm. In addition, we construct bearing-only dual-station and single-motion station target tracking systems, and the filtering performances of 5CPF and EKF-5CPF algorithms under different conditions are analyzed. The results show that both the 5CPF algorithm and EKF-5CPF have strong robustness and can adapt to different noise environments. Furthermore, both algorithms significantly outperform traditional nonlinear filtering algorithms in terms of convergence speed, tracking accuracy, and overall stability.

Keywords

Nonlinear filtering / Fifth-degree cubature particle filter / EKF-5CPF / Bearings-only target motion analysis

RIGHTS & PERMISSIONS

Harbin Engineering University and Springer-Verlag GmbH Germany, part of Springer Nature