A Correntropy Based Algorithm for Robust Localization in Wireless Networks

Abstract

Localization in wireless networks is possible by measuring some characteristics of the propagating signal related to the position of the user, which is always corrupted by noise components. In this paper, a correntropy based algorithm is proposed for localization and tracking of a mobile station in wireless networks. The performance of the proposed algorithm is compared with the Least Mean Square (LMS) and Least Mean P-norm (LMP) algorithms, and its preference aspects are discussed. The results show that, using correntropy can bring robustness to localization and improve performance in many realistic scenarios such as fat-tail noise distributions, one of the serious bottlenecks of the next-generation 5G wireless communications systems. In addition, it is shown that, the Gaussian kernel of the correntropy function reduces the sensitivity of the algorithm to the learning rate.