Geniş bant konum belirleme sistemi performans analizi ve iyileştirilmesi
Geniş bant spektrumu ve yüksek çözünürlük özellikleri ile çok geniş bant (Ultra-Wide Band, UWB) teknolojisi, birçok kapalı alan konum belirleme probleminde tercih edilmektedir. Bu çalışmada; UWB kablosuz sinyallerini kullanan konum belirleme sisteminin konum belirleme performansının belirlenmesi ve genellikle açık alan konum belirlemesinde kullanılan yatay hassaslık ölçeğinin (Horizontal Dilution Precision, HDOP) kapalı alanda bu sistem için kullanılmasıyla, konum tahmin hatasının iyileştirilmesi için yeni bir yöntem sunulmaktadır. Bu yöntem kullanışsız olan konum tahmin noktalarının elenmesi temel almaktadır ve UWB konum belirleme sistemi ile elde edilen deneysel konum belirleme sonuçlarının iyileştirilmesini sağlamaktadır. Bu yöntemin performans analizi için UWB sisteminin sağladığı konum verilerine literatürde kullanılan en küçük kareler (Least Squares, LS) ve doğrusal olmayan regresyon (Non-Linear Regression, NLR) ve önerilen HDOP tekniklerinin uygulanması ile elde edilen sonuçlar karşılaştırılmalı olarak sunulmaktadır. Sonuçlar göstermektedir ki; önerilen HDOP tekniği, LS algoritmasının ortalama konum hatasına göre yaklaşık olarak %9 oranında, NLR algoritmasına göre ise %5 oranında daha iyi sonuç vermektedir.
Performance analysis and improvement of the ultra-wide band localization system
With the very wide spectrum and high resolution characteristics, ultra-wideband (UWB) communication technique is chosen various indoor localization. This paper presents the localization performance of a positioning system, which uses UWB wireless signals, and a novel method to decrease the localization error using horizontal dilution of precision (HDOP), which uses for outdoor positioning in the literature, for this system. This method focuses to eliminate the unadaptable localization points and provides elimination of UWB experimental localization results. The comparison between the proposed method localizations and the results by elimination of UWB system raw localization data with the least-squares (LS) and the non-linear regression (NLR) techniques are provided for the performance analysis. As the results, the proposed HDOP technique approximately provides 9% the better performance than the LS algorithm. In addition, the technique also provides 5% the better performance than the NLR algorithm.
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