Kapalı Alanlarda Hassas UWB Tabanlı Konumlandırma için Uyarlamalı Genişletilmiş Kalman Filtresi

Year 2025, Volume: 13 Issue: 2, 692 - 705, 30.06.2025

Abdulhamit Sevgi , H. Erdinç Koçer

https://doi.org/10.29109/gujsc.1636087

Abstract

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.

Keywords

UWB, AEKF, Kapalı Alan Konumlandırma, IMU, Küresel Konumlandırma.

Thanks

Bu çalışma ilk yazarın doktora tezinin sonuçlarını içermektedir.

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

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.

Keywords

UWB, AEKF, Indoor Positioning, IMU, Global Positioning.

References

• [1] Sesyuk, S., Ioannou, S., & Raspopoulos, M. (2022). A survey of 3D indoor localization systems and technologies. Sensors, 22, 9380. https://doi.org/10.3390/s22239380
• [2] Arai, T., Yoshizawa, T., Aoki, T., Zempo, K., & Okada, Y. (2019). Evaluation of indoor positioning system based on attachable infrared beacons in metal shelf environment. 2019 IEEE International Conference on Consumer Electronics (ICCE), 1–4. https://doi.org/10.1109/ICCE.2019.8662007
• [3] Yue, L., Lei, H., & Qunli, X. (2021). Line-of-sight rates extraction of roll-pitch seeker under anti-infrared decoy state. Journal of Systems Engineering and Electronics, 32(1), 178–196. https://doi.org/10.23919/JSEE.2021.000016
• [4] Wang, J., Sun, S., Ning, Y., Zhang, M., & Pang, W. (2021). Ultrasonic TDoA indoor localization based on piezoelectric micromachined ultrasonic transducers. 2021 IEEE International Ultrasonics Symposium (IUS), 1–3. https://doi.org/10.1109/IUS52206.2021.9593813
• [5] Wang, J., Sun, S., Ning, Y., Zhang, M., & Pang, W. (2022). An ultra-low power, small size and high precision indoor localization system based on MEMS ultrasonic transducer chips. IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, 69(4), 1469–1477. https://doi.org/10.1109/TUFFC.2022.3148314
• [6] Chen, X., & Gao, Z. (2017). Indoor ultrasonic positioning system of mobile robot based on TDOA ranging and improved trilateral algorithm. 2017 2nd International Conference on Image, Vision and Computing (ICIVC), 923–927. https://doi.org/10.1109/ICIVC.2017.7984689
• [7] Sasikala, M., Athena, J., & Rini, A. S. (2021). Received signal strength based indoor positioning with RFID. 2021 IEEE International Conference on RFID Technology and Applications (RFID-TA), 260–263. https://doi.org/10.1109/RFID-TA53372.2021.9617439
• [8] Vena, A., Illanes, I., Alidieres, L., Sorli, B., & Perea, F. (2021). RFID based indoor localization system to analyze visitor behavior in a museum. 2021 IEEE International Conference on RFID Technology and Applications (RFID-TA), 183–186. https://doi.org/10.1109/RFID-TA53372.2021.9617265
• [9] López, Y., de Cos Gómez, M. E., & Las-Heras Andrés, F. (2017). A received signal strength RFID-based indoor location system. Sensors and Actuators A: Physical, 255, 118–133.
• [10] Milano, F., D’Errico, R., Monti, A., & Di Benedetto, M. G. (2024). BLE-based indoor localization: Analysis of some solutions for performance improvement. Sensors, 24(2).
• [11] Sophia, S., Shankar, B. M., Akshya, K., Arunachalam, A. C., Avanthika, V. T. Y., & Deepak, S. (2021). Bluetooth low energy based indoor positioning system using ESP32. 2021 Third International Conference on Inventive Research in Computing Applications (ICIRCA), 1698–1702. https://doi.org/10.1109/ICIRCA51532.2021.9544975
• [12] Yu, Y., Zhang, Y., Chen, L., & Chen, R. (2023). Intelligent fusion structure for Wi-Fi/BLE/QR/MEMS sensor-based indoor localization. Remote Sensing, 15(5), 1202.
• [13] Zhang, X., Sun, W., Zheng, J., Lin, A., Liu, J., & Ge, S. S. (2024). Wi-Fi-based indoor localization with interval random analysis and improved particle swarm optimization. IEEE Transactions on Mobile Computing, 23(10), 9120–9134. https://doi.org/10.1109/TMC.2024.3359669
• [14] Caso, G., De Nardis, L., Lemic, F., Handziski, V., Wolisz, A., & De Benedetto, M. G. (2020). ViFi: Virtual fingerprinting WiFi-based indoor positioning via multi-wall multi-floor propagation model. IEEE Transactions on Mobile Computing, 19(6), 1478–1491. https://doi.org/10.1109/TMC.2019.2908865
• [15] Fahama, H. S., Ansari-Asl, K., Kavian, Y. S., & Soorki, M. N. (2023). An experimental comparison of RSSI-based indoor localization techniques using ZigBee technology. IEEE Access, 11, 87985–87996. https://doi.org/10.1109/ACCESS.2023.3305396
• [16] Minh, T. L., & Xuan, D. T. (2021). Applying Kalman filter to UWB positioning with DS-TWR method in LOS/NLOS scenarios. 2021 International Symposium on Electrical and Electronics Engineering (ISEE), 95–99. https://doi.org/10.1109/ISEE51682.2021.9418707
• [17] Leng, J., Ma, G., Zhu, J., & Ma, H. (2021). Improved TDOA two-stage UWB localization algorithm for indoor mobile robot. 2021 IEEE International Conference on Recent Advances in Systems Science and Engineering (RASSE), 1–5. https://doi.org/10.1109/RASSE53195.2021.9686768
• [18] Wang, Q., Li, Z., Zhang, H., Yang, Y., & Meng, X. (2023). An indoor UWB NLOS correction positioning method based on anchor LOS/NLOS map. IEEE Sensors Journal, 23(24), 30739–30750. https://doi.org/10.1109/JSEN.2023.3328715
• [19] Lin, K.-H., Chen, H.-M., Li, G.-J., & Huang, S.-S. (2020). Analysis and reduction of the localization error of the UWB indoor positioning system. 2020 IEEE International Conference on Consumer Electronics - Taiwan (ICCE-Taiwan), 1–2. https://doi.org/10.1109/ICCE-Taiwan49838.2020.9258017
• [20] Santoro, L., Nardello, M., Brunelli, D., & Fontanelli, D. (2023). UWB-based indoor positioning system with infinite scalability. IEEE Transactions on Instrumentation and Measurement, 72, Article 1005711. https://doi.org/10.1109/TIM.2023.3282299
• [21] Van Herbruggen, B., Fontaine, J., & De Poorter, E. (2021). Anchor pair selection for error correction in time difference of arrival (TDOA) ultra wideband (UWB) positioning systems. Proceedings of the International Conference on Indoor Positioning and Indoor Navigation, 1–8.
• [22] Elsanhoury, M., Abotaleb, M., Elnainay, M., Badawy, M., & Hamouda, W. (2022). Precision positioning for smart logistics using ultra-wideband technology-based indoor navigation: A review. IEEE Access, 10, 44413–44445.
• [23] Jun, C., & Shibiao, H. (2018). Comparison and analysis of the error of multiple transceiver mode of TOA based on Kalman filter in IR-UWB system. 2018 IEEE International Conference of Safety Produce Informatization (IICSPI), 71–75. https://doi.org/10.1109/IICSPI.2018.8690338
• [24] Nguyen, D. T. A., Kim, K. S., Jeong, H. J., & Kim, H. (2020). Convolutional neural network-based UWB system localization. 2020 International Conference on Information and Communication Technology Convergence (ICTC), 488–490. https://doi.org/10.1109/ICTC49870.2020.9289326
• [25] Eang, C., & Lee, S. (2024). An integration of deep neural network-based extended Kalman filter (DNN-EKF) method in ultra-wideband (UWB) localization for distance loss optimization. Sensors, 24(23), 7643. https://doi.org/10.3390/s24237643
• [26] Zhang, C., Bao, X., Wei, Q., Ma, Q., Yang, Y., & Wang, Q. (2016). A Kalman filter for UWB positioning in LOS/NLOS scenarios. 2016 Fourth International Conference on Ubiquitous Positioning, Indoor Navigation and Location Based Services (UPINLBS), 73–78. https://doi.org/10.1109/UPINLBS.2016.7809953
• [27] Zhong, S., Zhang, K., Zhu, G., & Liu, S. (2018). UWB-inertial fusion location algorithm based on Kalman filtering. 2018 15th International Conference on Control, Automation, Robotics and Vision (ICARCV), 574–578. https://doi.org/10.1109/ICARCV.2018.8581294
• [28] Trifunović, M., Papp, I., & Bašičević, I. (2024). Use of extended Kalman filter in an UWB based indoor positioning system. 2024 Zooming Innovation in Consumer Technologies Conference (ZINC), 204–208. https://doi.org/10.1109/ZINC61849.2024.10579430
• [29] Zhang, F., Yang, L., Liu, Y., Ding, Y., Yang, S.-H., & Li, H. (2022). Design and implementation of real-time localization system (RTLS) based on UWB and TDoA algorithm. Sensors, 22(12), 4353. https://doi.org/10.3390/s22124353