6533b835fe1ef96bd129ec1a

RESEARCH PRODUCT

Cluster-Based RF Fingerprint Positioning Using LTE and WLAN Outdoor Signals

Tapani RistaniemiJussi TurkkaRiaz Mondal

subject

ta113PercentileK-nearest neighborComputer sciencebusiness.industrycell-IDFingerprint (computing)Real-time computingFingerprint recognitionGridHandsetlaw.inventionminimization of drive testsEuclidean distanceLTElawEmbedded systemgrid-based RF fingerprintingRadio frequencyCluster analysisbusinessfuzzy C-meanshierarchical clustering

description

In this paper we evaluate user-equipment (UE) positioning performance of three cluster-based RF fingerprinting methods using LTE and WLAN signals. Real-life LTE and WLAN data were collected for the evaluation purpose using consumer cellular-mobile handset utilizing ‘Nemo Handy’ drive test software tool. Test results of cluster-based methods were compared to the conventional grid-based RF fingerprinting. The cluster-based methods do not require grid-cell layout and training signature formation as compared to the gridbased method. They utilize LTE cell-ID searching technique to reduce the search space for clustering operation. Thus UE position estimation is done in short time with less computational cost. Among the cluster-based methods Agglomerative Hierarchical Cluster based RF fingerprinting provided best positioning accuracy using a single LTE and six WLAN signal strengths. This method showed an improvement of 42.3 % and 39.8 % in the 68th percentile and 95th percentile of positioning error (PE) over the grid-based RF fingerprinting. peerReviewed

yearjournalcountryeditionlanguage
2015-12-01
http://urn.fi/URN:NBN:fi:jyu-201712114592