6533b828fe1ef96bd1289069

RESEARCH PRODUCT

Cluster-based RF fingerprint positioning using LTE and WLAN signal strengths

Tapani RistaniemiRiaz Uddin MondalJussi Turkka

subject

Computer Networks and CommunicationsComputer scienceReal-time computingK-means clustering02 engineering and technologySignallaw.inventionK-nearest neighbors0203 mechanical engineeringlaw0202 electrical engineering electronic engineering information engineeringfuzzy C-means clusteringWi-FiElectrical and Electronic EngineeringData collectionbusiness.industryFingerprint (computing)k-means clusteringRF fingerprint positioning020206 networking & telecommunications020302 automobile design & engineeringGridHardware and ArchitectureEmbedded systemMinificationRadio frequencybusinesshierarchical clustering

description

Wireless Local Area Network (WLAN) positioning has become a popular localization system due to its low-cost installation and widespread availability of WLAN access points. Traditional grid-based radio frequency (RF) fingerprinting (GRFF) suffers from two drawbacks. First it requires costly and non-efficient data collection and updating procedure; secondly the method goes through time-consuming data pre-processing before it outputs user position. This paper proposes Cluster-based RF Fingerprinting (CRFF) to overcome these limitations by using modified Minimization of Drive Tests data which can be autonomously collected by cellular operators from their subscribers. The effect of environmental changes and device variation on positioning accuracy has been carried out. Experimental results show that even under these variations CRFF can improve positioning accuracy by 15.46 and 22.30% in 95 percentile of positioning error as compared to that of GRFF and K-nearest neighbour methods respectively. peerReviewed

yearjournalcountryeditionlanguage
2017-08-16
http://urn.fi/URN:NBN:fi:jyu-201712114595