Improving Particle Filter with Better Proposal Distribution for Nonlinear Filtering Problems
Designing better proposal distributions can greatly affect the performance of the particle filters, which has been extensively studied in the literature. In this paper, we propose to design better proposal distribution using a new version of unscented Kalman filter- the iterated unscented Kalman filter (IUKF). The IUKF makes use of both statistical and analytical linearization techniques in different steps of the filtering process, which makes it a better candidate for designing proposal distribution in particle filter framework. Each particle is updated using an iterative manner. Through this process, the algorithm can make better use of the current observation for state estimation. To evaluate the performance of the proposed particle filter, we use a synthetic model and a real-world model for the experiments. The experimental results have shown that the proposed algorithm outperforms the alternatives.
KeywordsParticle Filter Proposal Distribution Iterated Unscented Kalman filter Option Pricing
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