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Journals / Gazi Üniversitesi Fen Bilimleri Dergisi Part C: Tasarım ve Teknoloji / 2026 / Cilt: 13 Sayı: 2

Adaptive Extended Kalman Filter for Precise UWB-Based Localization in Indoor Areas

Kapalı Alanlarda Hassas UWB Tabanlı Konumlandırma için Uyarlamalı Genişletilmiş Kalman Filtresi

Abdulhamit Sevgi(OSTIM TECHNICAL UNIVERSITY)0000-0003-3567-848X
H. Erdinç Koçer(SELÇUK ÜNİVERSİTESİ)0000-0002-0799-2140
Pages
692–705
Publication Date
2025-06-30
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Experimental results show that AEKF provides higher positioning accuracy and offers real-time operability.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:The inadequacy of technologies such as Global Positioning System (GPS) in indoor spaces necessitates the development of alternative methods in the location determination processes of robotic systems.
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Key Findings & Data:The proposed method increases the positioning performance by applying AEKF to UWB and Inertial Measurement Unit (IMU) data collected from the system while preserving data integrity in case of complex system noise and incomplete measurement information in UWB-based positioning systems.
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Conclusion & Impact:Experimental results show that AEKF provides higher positioning accuracy and offers real-time operability.
🔬Design:Araştırma Makalesi
📍Scope:Comprehensive Sample
⚙️Variables:UWB · AEKF · Kapalı Alan Konumlandırma

Abstract

The inadequacy of technologies such as Global Positioning System (GPS) in indoor spaces necessitates the development of alternative methods in the location determination processes of robotic systems. In this field, technologies such as Ultra Wide Band (UWB), Wi-Fi Based Positioning Systems, Bluetooth Low Energy (BLE) beacons, Radio Frequency Identification (RFID), ultrasonic positioning and visual-based positioning are used. Although UWB technology is widely used for positioning, fluctuations and deviations in location accuracy occur due to factors such as multipath propagation, line of sight obstacles (NLOS), electromagnetic interference and sensor noise. In this study, an Adaptive Extended Kalman Filter (AEKF) based method is proposed to eliminate these problems. The proposed method increases the positioning performance by applying AEKF to UWB and Inertial Measurement Unit (IMU) data collected from the system while preserving data integrity in case of complex system noise and incomplete measurement information in UWB-based positioning systems. Experimental studies of the system were performed and compared as AEKF and filterless UWB measurements via a mobile robot. Experimental results show that AEKF provides higher positioning accuracy and offers real-time operability. These findings reveal that AEKF provides an effective solution.

Özet

Kapalı mekanlarda Küresel Konumlandırma Sistemi (GPS) gibi teknolojilerin yetersizliği, robotik sistemlerin konum belirleme süreçlerinde alternatif yöntemlerin geliştirilmesini gerekli kılmaktadır. Bu alanda, Ultra Geniş Bant (UWB), Wi-Fi Tabanlı Konumlandırma Sistemleri, Bluetooth Düşük Enerji (BLE) işaretçileri, Radyo Frekansı ile Tanımlama (RFID), ultrasonik konumlandırma ve görsel tabanlı konumlandırma gibi teknolojiler kullanılmaktadır. UWB teknolojisi konumlandırma için yaygın olarak kullanılmasına rağmen, çok yollu yayılım, görüş hattı engelleri (NLOS), elektromanyetik girişim ve sensör gürültüsü gibi etkenler nedeniyle konum doğruluğunda dalgalanmalar ve sapmalar meydana gelmektedir. Bu çalışmada, bu sorunları gidermek amacıyla Uyarlamalı Genişletilmiş Kalman Filtresi (AEKF) tabanlı bir yöntem önerilmektedir. Önerilen yöntem, UWB tabanlı konumlandırma sistemlerinde karmaşık sistem gürültüsü ve eksik ölçüm bilgileri durumunda veri bütünlüğünü koruyarak sistemden toplanan UWB ve Ataletsel Ölçüm Birimi (IMU) verilerine AEKF uygulayarak konumlandırma performansını artırmaktadır. Sistemin deneysel çalışmaları bir mobil robot aracılığıyla AEKF ve filtresiz UWB ölçümleri olarak yapılmış ve karşılaştırılmıştır. Deneysel sonuçlar, AEKF'nin daha yüksek konumlandırma doğruluğu sağladığını ve gerçek zamanlı çalışabilirlik sunduğunu göstermektedir. Bu bulgular, AEKF'nin etkin bir çözüm sunduğunu ortaya koymaktadır.

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Cite This Article

Sevgi, A., Koçer, H. E. (2025). Adaptive Extended Kalman Filter for Precise UWB-Based Localization in Indoor Areas. Gazi Üniversitesi Fen Bilimleri Dergisi Part C: Tasarım ve Teknoloji, Cilt: 13 Sayı: 2, 692–705. https://doi.org/10.29109/gujsc.1636087

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