Abstract
As a part of the multi-source cooperative navigation scheme, data fusion has a significant impact on the quality of state estimation. Particle filtering has gradually become the focus of many fusion methods due to its unique theoretical advantages in nonlinear non-Gaussian systems. However, the particle degradation and the resulting sample impoverishment restrict its application in complex engineering scenarios. In this paper, a robust cubature fission particle filter (RCFPF) is proposed to deal with these problems. First, in the framework of cubature rule, Huber function is used to combine the L2 norm and L1 norm to improve the importance density function(IDF), suppress the observation noise. Meanwhile, the proposed distribution(PD) is further optimized by combining the Gaussian distribution with Laplace distribution to alleviate particle degradation. Second, the particle swarm is fissioned before resampling, and the particle weight is reconstructed by fission of high weight particles and covering low weight particles to inhibit sample impoverishment. The vehicle experiments of multi-source cooperative navigation show that the proposed algorithm achieves better test results in accuracy and robustness than extended Kalman filter (EKF), strong tracking particle filter (STPF), and cubature particle filter (CPF).
Highlights
As a part of the multi-source cooperative navigation scheme, data fusion has a significant impact on the quality of state estimation
Particle degradation means that after multiple iterations, only a small number of particles in the particle swarm have large weights, while most of the particles have negligible weights, so that a lot of calculation time and resources are wasted on particles with small weights, which affects the performance of the a lgorithm[19–21]
Based on cooperative navigation as a platform, this paper proposes a robust cubature fission particle filter for multi-source data fusion
Summary
As a part of the multi-source cooperative navigation scheme, data fusion has a significant impact on the quality of state estimation. Since the particle weight is proportional to the likelihood function, choosing the equation of state as the IDF will make the variance of the particle weight larger, especially when the likelihood function is relatively "steep" or located at the tail of the state transition probability distribution, it will accelerate the degradation and even cause the filter d ivergence[27] To solve this problem, related scholars introduced the annealing algorithm to optimize the probability density[28,29]. The number of particles in the likelihood region is increased, the loop process introduced by the annealing optimization greatly increases the computational load and limits the practical application of PF Another idea of IDF selection is to introduce measurement, i.e., using existing filtering methods to make the a priori distribution and the likelihood function have a larger overlap area through the measurement so as to better match the posterior distribution. Havangi uses a particle swarm optimization algorithm to improve UKF, adjust the position and velocity of the particle swarm to improve the accuracy of the posterior probability of particle simulation[36]
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