Abstract
A particle filter is a powerful tool for object tracking based on sequential Monte Carlo methods under a Bayesian estimation framework. A major challenge for a particle filter in object tracking is how to allocate particles to a high-probability density area. A particle filter does not take into account the historical prior information on the generation of the proposal distribution and, thus, it cannot approximate posterior density well. Therefore, a new fuzzy grey prediction-based particle filter (called FuzzyGP-PF) for object tracking is proposed in this paper. First, a new prediction model which was based on fuzzy mathematics theory and grey system theory was established, coined the Fuzzy-Grey-Prediction (FGP) model. Then, the history state sequence is utilized as prior information to predict and sample a part of particles for generating the proposal distribution in the particle filter. Simulations are conducted in the context of two typical maneuvering motion scenarios and the results indicate that the proposed FuzzyGP-PF algorithm can exhibit better overall performance in object tracking.
Highlights
Object tracking has become a very important research topic for many years since it can be used in many applications [1,2]
A particle filter is a powerful tool for object tracking based on sequential Monte Carlo methods under a Bayesian estimation framework [12]
Since the grey prediction algorithm is able to predict the system state based on historical measurements other than establishing an a priori dynamic model, the GP-PF can significantly alleviate the sample degeneracy problem, which is common in standard particle filter (SPF), especially when it is used for object target tracking
Summary
Object tracking has become a very important research topic for many years since it can be used in many applications [1,2]. An important strategy to overcome this problem is to design better proposal distributions One such approach is the auxiliary particle filter (APF) [16] which improves some deficiencies of the SPF algorithm when dealing with tailed observation densities. A new approach has been proposed [22,23] This approach incorporates grey prediction [24] into particles filter (called GP-PF) based on grey system theory, in order to solve the sample degeneracy problem. Since the grey prediction algorithm is able to predict the system state based on historical measurements other than establishing an a priori dynamic model, the GP-PF can significantly alleviate the sample degeneracy problem, which is common in SPF, especially when it is used for object target tracking.
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