Abstract

In this paper we develop the Multiple Model Particle Filter (MMPF) for nonlinear systems. The particle filter is used to estimate the conditional probability for the modes while the Conjugate Unscented Transform (CUT) based Kalman filter is used to estimate the dynamical state of the system. The resultant particle filter is a bank of nonlinear filters with the sequence of modes given by the samples. A tracking example is used to illustrate the working and efficacy of the MMPF with respect to the Interactive Multiple Model (IMM) filter. Comparison results are shown using the Extended Kalman filter and Conjugate Unscented Kalman filter for both the MMPF and IMM filters.

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