Multipath Estimation Based on Centered Error Entropy Criterion for Non-Gaussian Noise

Abstract

With the advance of software receiver, multipath estimation becomes a key issue for high accuracy positioning systems. It is crucial for eliminating the multipath error and improving the positioning accuracy to estimate multipath parameters. The accessible multipath estimation algorithms are usually designed for Gaussian noise, and their performances degrade dramatically in non-Gaussian noise, since the mean square error criterion is adopted. To tackle the problem, a new filter based on centered error entropy criterion (CEEC) is proposed for multipath estimation. In the proposed filter, the CEEC is considered as a performance index, which is not limited to the assumption of Gaussian and linearity. According to a stochastic information gradient method, an optimal filer gain matrix is obtained by maximizing the performance function of centered error entropy. Meanwhile, a convergence analysis of the proposed filter is offered. Furthermore, a recursive estimation method based on modified Parzen windowing technique is proposed for practical implementation. The simulation results indicate that the proposed filter outperforms the filter based on minimum error entropy criterion for multipath estimation.

Keywords

• Multipath estimation
• centered error entropy criterion (CEEC)
• minimum error entropy criterion (MEEC)
• stochastic information gradient (SIG)