A Hybrid, Coupled Approach to the Continuous-Discrete Kalman Filter

In this letter we present a novel approach to continuous-discrete (CD) Kalman filtering. Unlike the EKF or other hybrid UKF-EKF filters, the novel approach does not require direct calculation of system Jacobians and instead uses unscented transforms (UTs) to extract a pair of matrices, each made up of a linear combination of derivatives (with respect to the state), that are used in its place. More specifically, they are used (1) in parts of the filtering process, and (2) in the linearly implicit numerical integration scheme of the filter’s state propagation stage. Extracting these matrices from the filter’s UTs and using them in both the filtering process and model simulation, or what we refer to as coupling the filter to model simulation, avoids having to calculate further function evaluations for standard implicit methods, making the process of state estimation for stiff systems more efficient. Another benefit of the proposed approach is that it offers UKF accuracy levels but improved numerical stability over the UKF. This is because it uses symmetric and positive-definite (PD) representations of the state covariance propagation and measurement update equations.

[1]  Rudolph van der Merwe,et al.  The square-root unscented Kalman filter for state and parameter-estimation , 2001, 2001 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.01CH37221).

[2]  Fredrik Gustafsson,et al.  Some Relations Between Extended and Unscented Kalman Filters , 2012, IEEE Transactions on Signal Processing.

[3]  John B. Moore,et al.  Optimal State Estimation , 2006 .

[4]  M. V. Kulikova,et al.  Accurate continuous-discrete unscented Kalman filtering for estimation of nonlinear continuous-time stochastic models in radar tracking , 2017, Signal Process..

[5]  M. V. Kulikova,et al.  Square-root accurate continuous-discrete extended-unscented Kalman filtering methods with embedded orthogonal and J-orthogonal QR decompositions for estimation of nonlinear continuous-time stochastic models in radar tracking , 2020, Signal Process..

[6]  Jeffrey K. Uhlmann,et al.  New extension of the Kalman filter to nonlinear systems , 1997, Defense, Security, and Sensing.

[7]  E. Eitelberg Numerical simulation of stiff systems with a diagonal splitting method , 1979 .

[8]  Lotfi Senhadji,et al.  Various Ways to Compute the Continuous-Discrete Extended Kalman Filter , 2012, IEEE Transactions on Automatic Control.

[9]  Simo Särkkä,et al.  On Unscented Kalman Filtering for State Estimation of Continuous-Time Nonlinear Systems , 2007, IEEE Trans. Autom. Control..

[10]  Thomas Mazzoni,et al.  Computational aspects of continuous–discrete extended Kalman-filtering , 2008, Comput. Stat..

[11]  Mohinder S. Grewal,et al.  Kalman Filtering: Theory and Practice Using MATLAB , 2001 .

[12]  Simon Haykin,et al.  Cubature Kalman Filtering for Continuous-Discrete Systems: Theory and Simulations , 2010, IEEE Transactions on Signal Processing.