Abstract
A modification of N-IPS, a well known particle filter method is proposed and is shown to be more efficient than the baseline algorithm in the small sample size limit and when the observations are ``reliable''. The algorithm called LS-N-IPS adds local search to the baseline algorithm: in each time step the predictions are refined in a local search procedure that utilizes the most recent observation. The uniform stability of LS-N-IPS is studied and results of experiments are reported both for a simulated and a real-world (visual) tracking problem.
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