NDLI: Distributed estimation using online semi-supervised particle filter for mobile sensor networks

Content Provider	IET Digital Library
Author	Yoo, Jaehyun Kim, Woojin Kim, Hyoun Jin
Abstract	This study proposes an improved particle filter by incorporating semi-supervised machine learning for location estimation in mobile sensor networks (MSNs). A time-varying prior model is learned online as the likelihood of particle filter in order to adapt to dynamic characteristics of state and observation. Thanks to semi-supervised learning, the proposed particle filter can improve efficiency and accuracy, where the amount of available labelled training data is limited. The authors compare the proposed algorithm with the particle filter based on supervised learning. The algorithms are evaluated for received signal strength indicator (RSSI)-based distributed location estimation for MSN in which communication bandwidth and accuracy of the range measurement are limited. First, experimental results show that the semi-supervised algorithm can learn suddenly-changed RSSI characteristics while the supervised learning cannot. Second, the proposed particle filter is more accurate and robust against variations of the environment such as new obstacle configurations. Furthermore, the suggested particle filter shows low statistical variability during repeated experiments, confirmed by much smaller error deviation than the compared particle filter.
Starting Page	418
Ending Page	427
Page Count	10
ISSN	17518644
Volume Number	9
e-ISSN	17518652
Issue Number	Issue 3, Feb (2015)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-cta/9/3
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-cta.2014.0495
Journal	IET Control Theory & Applications
Publisher Date	2015-01-29
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Available Labelled Training Data Communication Bandwidth Communication Computing Distributed Estimation Knowledge Engineering Technique Learning in AI Mobile Sensor Network MSN Obstacle Configurations Online Semisupervised Particle Filter Particle Filter Received Signal Strength Indicator-based Distributed Location Estimation Semisupervised Machine Learning Statistical Analysis Statistical Variability Statistics Suddenly-changed RSSI Characteristic Telecommunication Computing Time-varying Prior Model Wireless Sensor Network
Content Type	Text
Resource Type	Article
Subject	Control and Optimization Control and Systems Engineering Human-Computer Interaction Electrical and Electronic Engineering Computer Science Applications

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