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 ...

To address sparse channel estimation problem in nonGaussian impulsive noise environment, a recursive maximum correntropy criteria (RMCC) algorithm using sparse constraint is proposed to combat impulsive-inducing instability. Specifically, the recursive algorithm on the correntrioy with a forgetting factor of error at iteration is to solve steady-state error for improving the maximum correntropy...

A general zero attraction (GZA) proportionate normalized maximum correntropy criterion (GZA-PNMCC) algorithm is devised and presented on the basis of the proportionate-type adaptive filter techniques and zero attracting theory to highly improve the sparse system estimation behavior of the classical MCC algorithm within the framework of the sparse system identifications. The newly-developed GZA-...

Non-Gaussian noise is common in industrial applications, and it a severe challenge to existing state estimators. In this paper, novel robust maximum correntropy finite impulse response (MCFIR) filter proposed deal with the estimation problem linear state-space system corrupted by outliers. The operates as memory form, thus obtains superior immunity statistics process uncertainties than Kalman-l...

This letter proposes a new multiple linear regression model using regularized correntropy for robust pattern recognition. First, we motivate the use of correntropy to improve the robustness of the classical mean square error (MSE) criterion that is sensitive to outliers. Then an l regularization scheme is imposed on the correntropy to learn robust and sparse representations. Based on the half-q...