In this paper, we present a novel high-degree cubature particle filter for nonlinear systems with missing measurements to limit particle degradation. For the proposed particle filter, we derive the explicit formulae for the importance function and its corresponding weights, which take into account missing measurements. To fulfill the numerical integrals calculation of the formulae in the importance function, we employed the fifth-degree spherical-radial cubature rule to give the high-degree cubature particle filtering algorithm. A simulation example shows that the high-degree cubature particle filter increases the effective sample size and improves estimation accuracy compared with the existing bootstrap particle filter, extended particle filter, and unscented particle filter for the nonlinear systems under discussion.
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