Design of robust cubature fission particle filter algorithm in multi-source cooperative navigation

Area of Science:

• Navigation Systems
• Data Fusion
• State Estimation

Background:

• Data fusion is critical for state estimation quality in multi-source cooperative navigation.
• Particle filtering excels in nonlinear, non-Gaussian systems but suffers from particle degradation and sample impoverishment.
• These limitations hinder particle filter application in complex engineering scenarios.

Purpose of the Study:

• To propose a robust cubature fission particle filter (RCFPF) to overcome particle degradation and sample impoverishment.
• To enhance the performance of particle filters in multi-source cooperative navigation systems.

Main Methods:

• Utilized cubature rule framework with Huber function to improve the importance density function (IDF) and suppress observation noise.
• Optimized proposed distribution (PD) by combining Gaussian and Laplace distributions to mitigate particle degradation.
• Implemented particle swarm fission before resampling, reconstructing weights to inhibit sample impoverishment.

Main Results:

• The RCFPF algorithm demonstrated superior accuracy and robustness compared to Extended Kalman Filter (EKF), Strong Tracking Particle Filter (STPF), and Cubature Particle Filter (CPF).
• Vehicle experiments validated the effectiveness of the proposed RCFPF in multi-source cooperative navigation.

Conclusions:

• The RCFPF effectively addresses particle degradation and sample impoverishment in particle filtering.
• The proposed method offers significant improvements in state estimation accuracy and robustness for cooperative navigation applications.