A CORRENTROPY BASED ALGORITHM FOR ROBUST LOCALIZATION IN WIRELESS NETWORKS

被引：1

作者：

Sedighizad, Mahboobeh ^{[1
]}

Seyfe, Babak ^{[1
,2
]}

Valaee, Shahrokh ^{[2
]}

机构：

[1] Shahed Univ, Dept Elect Engn, Informat Theoret Learning Syst Lab ITLSL, Tehran, Iran

[2] Univ Toronto, Dept Elect & Comp Engn, Toronto, ON, Canada

来源：

2021 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP 2021) | 2021年

关键词：

Localization; correntropy; Gaussian mixture noise; LOCATION; SQUARES;

D O I：

10.1109/ICASSP39728.2021.9414143

中图分类号：

O42 [声学];

学科分类号：

070206 ; 082403 ;

摘要：

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.

引用

页码：4660 / 4664

页数：5