Estimation of maneuvering target in the presence of non-Gaussian noise: A coordinated turn case study

This paper explores performance of various methods for state estimation of radar tracking models. A coordinated turn case study of maneuvering target in the presence of non-Gaussian noise is of particular interest. We aim at evaluating the estimation potential of recently presented filters grounded in the Maximum Correntropy Criterion (MCC). Various investigations confirm the outstanding performance of such filters for treating stochastic systems disturbed with impulsive (shot) and mixed-Gaussian noises. However, those filters are intended for linear models, and the success of the MCC-based state estimation in a nonlinear continuous-time stochastic environment, which often underlies radar tracking modeling, is still debatable. First, we extend the MCC-based filters, which are designed presently for linear discrete-time stochastic models, to nonlinear continuous-discrete systems. We devise the conventional (non-square-root) filtering and its square-root version as well. Second, we fulfil a comprehensive examination of these new methods in severe conditions of tackling a seven-dimensional radar tracking problem, where an aircraft executes a coordinated turn, in the presence of both impulsive (shot) and mixed-Gaussian noises. In addition, the novel MCC-based filters are compared to various contemporary extended, cubature and unscented Kalman-like state estimators.