Novel Robust Indoor Device-Free Moving-Object Localization and Tracking Using Machine Learning With Kalman Filter and Smoother-Reference-Cited by-同舟云学术

Novel Robust Indoor Device-Free Moving-Object Localization and Tracking Using Machine Learning With Kalman Filter and Smoother

Published:2022-12 Issue:4 Volume:16 Page:6253-6264
ISSN:1932-8184
Container-title:IEEE Systems Journal
language:
Short-container-title:IEEE Systems Journal

Author:

Liu Guannan¹,Neupane Prasanga¹,Wu Hsiao-Chun¹^ORCID,Xiang Weidong²,Pu Limeng³,Chang Shih Yu⁴^ORCID

Affiliation:

1. School of Electrical Engineering and Computer Science, Louisiana State University, Baton Rouge, LA, USA

2. Department of Electrical and Computer Engineering, University of Michigan, Dearborn, MI, USA

3. Center for Computation and Technology, Louisiana State University, Baton Rouge, LA, USA

4. Department of Applied Data Science, San Jose State University, San Jose, CA, USA

Funder

Louisiana Board of Regents Research Competitiveness Subprogram

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Control and Systems Engineering,Computer Networks and Communications,Computer Science Applications,Information Systems

Link

http://xplorestaging.ieee.org/ielx7/4267003/9979155/09864658.pdf?arnumber=9864658

Reference53 articles.

1. A Novel Indoor Ranging Algorithm Based on a Received Signal Strength Indicator and Channel State Information Using an Extended Kalman Filter

2. A Sensor Fusion Framework for Indoor Localization Using Smartphone Sensors and Wi-Fi RSSI Measurements

3. On Number of Tags Estimation in RFID Systems

4. Forecasting of Power Corporations’ Default Probability With Nonlinear Kalman Filtering

5. A Robust Visual Human Detection Approach With UKF-Based Motion Tracking for a Mobile Robot

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