A Comparative Study of Kalman-like Filters for State Estimation of Turning Aircraft in Presence of Glint Noise

Abstract

This paper continues the study started by Kulikov and Kulikova on state estimation accuracies of various Kalman-like filtering techniques in target tracking scenarios with non-Gaussian noise in 2018. The cited authors examined a number of methods, which are grounded in the minimum-variance or maximum-correntropy criteria and cover extended-, cubature- and unscented-type Kalman filters, in the well-known turning aircraft scenario with impulsive (shot) noise or mixed-Gaussian one. Despite the success of the maximum-correntropy-based filtering methods reported on estimation of linear discrete-time stochastic systems in literature, those case studies expose the superiority of the cubature and unscented Kalman filters towards various extended Kalman methods designed in the minimum-variance sense or grounded in the maximum-correntropy criterion within the mentioned target tracking scenarios. Here, we extend that examination to the turning aircraft scenario with glint noise, which is simulated by a sum of two zero-mean Gaussian variables with difference covariances. In particular, our study reveals a valued potential of the maximum-correntropy-based accurate continuous-discrete extended Kalman filters devised by the above authors in this glint noise state estimation environment.