Maximum Correntropy Based Unscented Particle Filter for Cooperative Navigation with Heavy-Tailed Measurement Noises

Maximum correntropy unscented filter

Xi Liu, Badong Chena∗, Bin Xu, Zongze Wu, and Paul Honeine School of Electronic and Information Engineering, Xi’an Jiaotong University, Xi’an, China; School of Automation, Northwestern Polytechnical University, Xi’an, China; School of Electronic and Information Engineering, South China University of Technology, Guangzhou, China; the Normandie Univ, UNIROUEN, UNIHAVRE, INSA Rouen, LITIS, Rouen, ...

Maximum Correntropy Unscented Kalman Filter for Spacecraft Relative State Estimation

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 ...

Maximum Correntropy Kalman Filter

—Traditional Kalman filter (KF) is derived under the well-known minimum mean square error (MMSE) criterion, which is optimal under Gaussian assumption. However, when the signals are non-Gaussian, especially when the system is disturbed by some heavy-tailed impulsive noises, the performance of KF will deteriorate seriously. To improve the robustness of KF against impulsive noises, we propose in ...

The Unscented Particle Filter

In this paper, we propose a new particle filter based on sequential importance sampling. The algorithm uses a bank of unscented filters to obtain the importance proposal distribution. This proposal has two very "nice" properties. Firstly, it makes efficient use of the latest available information and, secondly, it can have heavy tails. As a result, we find that the algorithm outperforms standar...

Robust Adaptive Unscented Particle Filter