Abstract
The prior covariance estimation method based on inverse covariance intersection (ICI) is proposed to apply the particle flow filter. The proposed method has better estimate performance and guarantees consistent estimation results compared with previous works. ICI is the recently developed method of ellipsoidal intersection and is used to get the combined estimate of prior covariance. This method integrates the sample covariance estimate, which is unbiased but usually with high variance, together with a more structured but typically a biased target covariance through fusion gains. For verifying the performance of the proposed algorithm, analysis and simulations are performed. Through the simulations, the results are given to illustrate the consistency and accuracy of the proposed algorithm’s estimation and target tracking performance.
Highlights
Recent researches in particle flow filters (PFFs) have provided a solution to avoid degeneracy problems [1]-[3]
As the Monte Carlo approximation of the posterior distribution is represented by a few particles, the weight degeneracy issue causes a poor representation of the posterior distribution [5]
A more general solution involves the incorporation of Markov Chain Monte Carlo (MCMC) methods within the particle filters [10]-[14]
Summary
Recent researches in particle flow filters (PFFs) have provided a solution to avoid degeneracy problems [1]-[3]. In the previous work [23], the key factors affecting the PFF performance are classified as pseudo-time discretization, ordinary differential equation (ODE) numerical solution, prior covariance estimation, and re-generating the particles set. The proposed method has better estimate performance and guarantees consistent estimation results compared with previous works of prior covariance estimation [28], [29]. The effect of the characteristics that changed the prior covariance written in Section II on the convergence of the filter in the prediction process of the proposed algorithm is analyzed.
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