Localization Error Analysis of Indoor Positioning System Based on UWB Measurements

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dc.contributor.authorPoulose, Alwin
dc.contributor.authorEyobu, Odongo Steven
dc.contributor.authorKim, Myeongjin
dc.contributor.authorSeog Han, Dong
dc.date.accessioned2023-02-03T16:31:00Z
dc.date.available2023-02-03T16:31:00Z
dc.date.issued2019
dc.description.abstractUltra wide band (UWB) systems use time information instead of the popular received signal strength indication (RSSI). UWB is known for its high position accuracy in localization. RSSI-based localization is easily affected by signal attenuation and has a poor localization accuracy as compared to the time of arrival (TOA) technique. In this paper, different localization algorithms for the UWB system were analytically reviewed. The performance of the localization algorithms is discussed in terms of root mean square and cumulative distribution function of localization errors. The experiment results demonstrate the effectiveness of different localization algorithms for UWB indoor positioning. The fingerprint estimation algorithm shows better performance compared to linearized least square estimation and weighted centroid estimation algorithms. The experimental results show that the linearized least square algorithm has poor performance for UWB indoor localization.en_US
dc.identifier.citationPoulose, A., Eyobu, O. S., Kim, M., & Han, D. S. (2019, July). Localization error analysis of indoor positioning system based on UWB measurements. In 2019 Eleventh International Conference on Ubiquitous and Future Networks (ICUFN) (pp. 84-88). IEEE.en_US
dc.identifier.isbn978-1-7281-1340-1
dc.identifier.urihttps://nru.uncst.go.ug/handle/123456789/7520
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.subjectIndoor localizationen_US
dc.subjectUltra wide banden_US
dc.subjectLeast square estimationen_US
dc.subjectFingerprint estimationen_US
dc.subjectWeighted centroid estimationen_US
dc.titleLocalization Error Analysis of Indoor Positioning System Based on UWB Measurementsen_US
dc.typeOtheren_US
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