Software analysis unifying particle filtering and marginalized particle filtering

Particle filtering has evolved into wide range of techniques giving rise to many implementations and specialized algorithms. In theory, all these techniques are closely related, however this fact is usually ignored in software implementations. In this paper, particle filtering is studied together with marginalized particle filtering and a generic software scheme unifying these two areas is proposed. It is presented in general terms of object-oriented programming so that it may be implemented in existing Bayesian filtering toolboxes that are briefly reviewed. The power of the approach is illustrated on a new variant of the marginalized particle filter. A range of new variants of the filter is obtained by plugging this class into the proposed software structure. The framework and the illustrative example is implemented in the BDM library.