A QUATERNION KERNEL MINIMUM ERROR ENTROPY ADAPTIVE FILTER

In this paper, we develop a kernel adaptive filter for quaternion data based on minimum error entropy cost function. We apply generalized Hamilton-real (GHR) calculus that is applicable to Hilbert space for evaluating the cost function gradient to develop the quaternion kernel minimum error entropy (MEE) algorithm. The MEE algorithm minimizes Renyis quadratic entropy of the error between the filter output and desired response or indirectly maximizing the error information potential. Here, the approach is applied to quaternions for improving performance for biased or non-Gaussian signals compared with the minimum mean square error criterion of the kernel least mean square algorithm. Simulation results are used to verify the performance of the algorithm. Convergence is very fast and is shown to out-perform existing algorithms.