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This paper continues our research devoted to an accurate nonlinear Bayesianfilters' design. Our solution implies numerical methods for solving ordinarydifferential equations (ODE) when propagating the mean and error covariance ofthe dynamic state. The key idea is that an accurate implementation strategyimplies the methods with a discretization error control involved. This meansthat the filters' moment differential equations are to be solved accurately,i.e. with negligible error. In this paper, we explore the continuous-discreteextended-cubature Kalman filter that is a hybrid method between Extended andCubature Kalman filters (CKF). Motivated by recent results obtained for thecontinuous-discrete CKF in Bayesian filtering realm, we propose the numericallystable (to roundoff) square-root approach within a singular value decomposition(SVD) for the hybrid filter. The new method is extensively tested on a fewapplication examples including stiff systems.

Square-root filtering via covariance SVD factors in the accurate continuous-discrete extended-cubature Kalman filter