On computational robustness of accurate continuous-discrete unscented Kalman filtering for target tracking models - Kulikova, Maria V.; Kulikov, Gennady Yu (2016 European Control Conference (ECC), 2016)
This paper presents a variable-stepsize unscented Kalman filter for treating continuous-time stochastic models in radar tracking, numerically. Our method is grounded in the Gauss-type nested implicit Runge-Kutta formula of order 6 applied for solving moment differential equations (MDEs). The built-in global error control ensures that the MDEs are integrated with negligible errors. The latter raises the accuracy of state estimation and makes our state estimator competitive to the accurate continuous-discrete extended Kalman filter (ACD-EKF) and the mixed-type accurate continuous-discrete extended-unscented Kalman filter (ACD-EUKF) developed earlier for target tracking models. The effectiveness of the new filter and its comparison to the ACD-EKF and ACD-EUKF are studied on a 7-dimensional radar tracking problem with both short and long waiting times. Our research suggests that the unscented Kalman filtering is more robust to the error accumulation and better suits for treating target tracking models with long sampling periods whereas the extended Kalman filtering is more accurate when the waiting time is short.