Fuzzy Grey Prediction-Based Particle Filter for Object Tracking

Abstract

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.

Optimal State Estimation via Adaptive Fuzzy Particle Filter

“…The fuzzy logic approach is one of the methods used to address this problem with particle filter's performance. In order to analyze and make inference about a dynamic system, at least two models are required: First, a model describing the evolution of the state with time (System Model) and, a model relating the noisy measurements to the state (Measurement Model) [15]. The general Fuzzy Logic architecture consists of four stages of handling: fuzzification, a knowledge base, inferences of the rules, and defuzzification as shown in fig.…”