Abstract
A new algorithm called maximum correntropy unscented Kalman filter (MCUKF) is proposed and applied to relative state estimation in space communication networks. As is well known, the unscented Kalman filter (UKF) provides an efficient tool to solve the non-linear state estimate problem. However, the UKF usually plays well in Gaussian noises. Its performance may deteriorate substantially in the presence of non-Gaussian noises, especially when the measurements are disturbed by some heavy-tailed impulsive noises. By making use of the maximum correntropy criterion (MCC), the proposed algorithm can enhance the robustness of UKF against impulsive noises. In the MCUKF, the unscented transformation (UT) is applied to obtain a predicted state estimation and covariance matrix, and a nonlinear regression method with the MCC cost is then used to reformulate the measurement information. Finally, the UT is adopted to the measurement equation to obtain the filter state and covariance matrix. Illustrative examples demonstrate the superior performance of the new algorithm.
Highlights
The relative state estimation problem is rather important in space communication networks, including inter-satellite link (ISL) establishment, communication support, multiple spacecraft formation flying, and spacecraft rendezvous and docking [1,2,3,4]
To improve the robustness of the unscented Kalman filter (UKF), we present the idea to combine the maximum correntropy criterion (MCC) cost function with the UKF framework to derive a novel UKF, which may perform much better in non-Gaussian noise environments, since correntropy contains second and higher order moments of the error
Those results illustrate that the UKF play the best performance in all filters in this case and the UKF type filters have the better performance than the extended Kalman filter (EKF) type counterparts
Summary
The relative state estimation problem is rather important in space communication networks, including inter-satellite link (ISL) establishment, communication support, multiple spacecraft formation flying, and spacecraft rendezvous and docking [1,2,3,4]. The main reason for this is that both methods are based on the minimum l2 -norm technique and exhibit sensitivity to heavy-tailed noises [14] To address this problem, some robust methods have been proposed. An optimization criterion based on correntropy, called the maximum correntropy criterion (MCC) [17,18], has recently been successfully applied in robust adaptive filtering in the presence of heavy-tailed non-Gaussian noises [17,28,29,30,31,32]. In MCUKF, the UT is applied to obtain a predicted state estimation and covariance matrix, and a non-linear regression model under MCC is used to reformulate the measurement information.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
