2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information
Login Paper Search My Schedule Paper Index Help

My ICASSP 2021 Schedule

Note: Your custom schedule will not be saved unless you create a new account or login to an existing account.
  1. Create a login based on your email (takes less than one minute)
  2. Perform 'Paper Search'
  3. Select papers that you desire to save in your personalized schedule
  4. Click on 'My Schedule' to see the current list of selected papers
  5. Click on 'Printable Version' to create a separate window suitable for printing (the header and menu will appear, but will not actually print)

Paper Detail

Paper IDSAM-12.2
Paper Title A CORRENTROPY BASED ALGORITHM FOR ROBUST LOCALIZATION IN WIRELESS NETWORKS
Authors Mahboobeh Sedighizad, Babak Seyfe, Information Theoretic Learning Systems Lab. (ITLSL), Dept. of Electrical Engineering, Shahed University, Iran; Shahrokh Valaee, University of Toronto, Canada
SessionSAM-12: Tracking and Localization
LocationGather.Town
Session Time:Friday, 11 June, 14:00 - 14:45
Presentation Time:Friday, 11 June, 14:00 - 14:45
Presentation Poster
Topic Sensor Array and Multichannel Signal Processing: [SAM-DOAE] Direction of arrival estimation and source localization
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
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.