Multipath estimation using kernel minimum error entropy filter
Cheng Lan, Ren Mifeng, Xie Gang, Jie Chen · 2016
Multipath is the dominant error source for high-accuracy positioning systems. It is significant for eliminating the multipath error and improving the positioning accuracy to estimate multipath parameters. The existing multipath estimation algorithms are usually designed for Gaussian noise, and their performances degrade dramatically in non-Gaussian noise. To solve the problem, a multipath estimation algorithm based on kernel minimum error entropy filter (KMEEF) is proposed. In KMEEF, the minimum error entropy (MEE) criterion instead of mean square error (MSE) criterion is applied, which is not limited to the assumption of Gaussian and linearity. According to the stochastic information gradient (SIG) method, an optimal filer gain matrix is obtained by minimizing the error entropy. Furthermore, the learning rate is suggested by a convergence analysis. The simulation results show the effectivity of the proposed algorithm for multipath estimation.