Hidden state estimation using the Correntropy Filter with fixed point update and adaptive kernel size
In this paper we review the Correntropy Filter for hidden state estimation and we introduce the fixed point update rule for the Correntropy Filter instead of using gradient ascent for faster convergence. We further propose an adaptive kernel bandwidth selection algorithm. It is shown that the new filter outperforms the Kalman Filter and has no free parameters. The algorithm's capabilities are demonstrated on a simulated experiment and a vehicle tracking problem.