Paper Info
Title
Fingerprint-Based Device-Free Localization Performance in Changing Environments
Abstract
Device-free localization (DFL) systems locate a person in an environment by measuring the changes in received signal on links in a wireless network. A fingerprint-based DFL method collects a training database of measurement fingerprints and uses a machine learning classifier to determine a person’s location from a new fingerprint. However, as the environment changes over time due to furniture or other objects being moved, the fingerprints diverge from those in the database. This paper addresses, for DFL methods that use received signal strength as measurements, the degradation caused as a result of environmental changes. We perform experiments to quantify how changes in an environment affect accuracy, through a repetitive process of randomly moving an item in a residential home and then conducting a localization experiment, and then repeating. We quantify the degradation as well as consider ways to be more robust to environmental change. We find that the localization error rate doubles, on average, for every six random changes in the environment. We find that the random forests classifier has the lowest error rate among four tested. We present a correlation method for selecting channels which decreases the localization error rate from 4.8% to 1.6%.
Year
DOI
Venue
2015
10.1109/JSAC.2015.2430515
IEEE Journal on Selected Areas in Communications
Keywords
Field
DocType
Training,Error analysis,Training data,Frequency measurement,Support vector machines,Vegetation,Databases
Wireless network,Data mining,Computer science,Real-time computing,Artificial intelligence,Classifier (linguistics),Random forest,Learning classifier system,Pattern recognition,Word error rate,Support vector machine,Communication channel,Fingerprint
Journal
Volume
Issue
ISSN
PP
99
0733-8716
Citations 
PageRank 
References 
14
0.61
25
Authors
3
Name
Order
Citations
PageRank
Brad Mager1140.61
Philip Lundrigan2140.61
Neal Patwari33805241.58