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Standard particle filters cannot handle dynamic systems with unknown fixed parameters. In this paper, we extend the recently proposed cost-reference particle filtering methodology (CRPF) to jointly estimate the time-varying state and the static parameters of a dynamic system. In particular, we introduce three strategies that allow assigning costs to the random samples in the state-space independently of the fixed parameters. Asymptotic results that illuminate the relationships among the methods are derived, and computer simulation results are presented to illustrate their practical implementation in a vehicle navigation problem.