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Determining Distance Measurement Accuracy by Pedometer, GNSS and IMU and the New Developed Acceleration Sensor Based Method

Yıl 2022, , 138 - 143, 28.06.2022
https://doi.org/10.46460/ijiea.1079781

Öz

Positioning applications have started to become widespread in recent years with the acquisition of the ability of mobile phones to determine location. While generally GNSS and positioning methods are used, as a result of the GNSS system not working efficiently in closed areas, efforts have been made to develop other methods for position determination. The basic working principle of these methods is to establish common local networks and to determine the location by measuring distances with these local networks. For this, Bluetooth, wireless networks and signals with different frequencies, ultrasonic signals, RFID modules working with radio frequencies, etc. systems are used. Both real-time data can be produced and the generated data can be saved and stored on servers and then offered to users. According to the technology used, data with high and medium position accuracy can be obtained. However, due to the high cost of these systems due to the need for networking and the inability to produce results in open areas, IMU-based only distance measuring solutions have been created, especially in sports applications. In the presented article, methods and filters have been developed for obtaining distance measurement, which is the basis of position determination, with high accuracy using only the acceleration sensor. The obtained results have compared with other distance measuring methods and the actual distance. The developed filters have been developed to prevent false step detection, filter static movements, and generally calculate the correct distance by reducing the noise of the acceleration sensor, based on variable stride length detection and distance measurement calculation. The amount of error reached 60% in the distance obtained with the acceleration sensor without using filters, the amount of error decreased below 2% with the developed method. These results clearly showed that the developed method can be used for distance measurement and sub-meter position determination.

Kaynakça

  • Hightower, J., Borriello, G. ve Want, R. (2000). SpotON: An Indoor 3D Location Sensing Technology Based on RF Signal Strength. UW CSE00-02
  • Ni, L. M., Yunhao Liu, Yiu Cho Lau ve Patil, A. P. (2003). LANDMARC: indoor location sensing using active RFID. Proceedings of the First IEEE International Conference on Pervasive Computing and Communications, 2003. (PerCom 2003)., 407–415. https://doi.org/10.1109 /PERCOM.2003.1192765
  • Jin, G., Lu, X. ve Park, M.-S. (2006). An Indoor Localization Mechanism Using Active RFID Tag. IEEE International Conference on Sensor Networks, Ubiquitous, and Trustworthy Computing -Vol 1 (SUTC’06), 1, 40–43. https://doi.org/10.1109/SUTC.2006.1636157
  • Bahl, P. ve Padmanabhan, V. N. (2000). RADAR: an in-building RF-based user location and tracking system. Proceedings IEEE INFOCOM 2000. Conference on Computer Communications. Nineteenth Annual Joint Conference of the IEEE Computer and Communications Societies (Cat. No.00CH37064), 2, 775–784. https://doi.org/10.1109/INFCOM.2000.832252
  • Ingram, S. J., Harmer, D. ve Quinlan, M. (2014). UltraWideBand indoor positioning systems and their use in emergencies. PLANS 2004. Position Location and Navigation Symposium (IEEE Cat. No.04CH37556), 706–715. https://doi.org/10.1109/PLANS.2004.1309063
  • Jiang L., Hoe L-N ve Loon L-L. (2010). Integrated UWB and GPS location sensing system in hospital environment. 2010 5th IEEE Conference on Industrial Electronics and Applications, 286–289. https://doi.org/10.1109/ICIEA.2010.5516828
  • Gigl, T., Janssen, G. J. M., Dizdarević, V., Witrisal, K. ve Irahhauten, Z. (2007). Analysis of a UWB indoor positioning system based on received signal strength. 4th Workshop on Positioning, Navigation and Communication 2007, WPNC’07 - Workshop Proceedings, 2007(1), 97–101. https://doi.org/10.1109/wpnc.2007.353618
  • Ni, L. M., Yunhao Liu, Yiu Cho Lau ve Patil, A. P. (2003). LANDMARC: indoor location sensing using active RFID. Proceedings of the First IEEE International Conference on Pervasive Computing and Communications, 2003. (PerCom 2003)., 407–415. https://doi.org/10.1109/PERCOM.2003.1192765
  • Hazas, M. ve Hopper, A. (2006). Broadband ultrasonic location systems for improved indoor positioning. IEEE Transactions on Mobile Computing, 5(5), 536–547. https://doi.org/10.1109/TMC.2006.57
  • Roos, T., Myllymäki, P., Tirri, H., Misikangas, P. ve Sievänen, J. (2002). A Probabilistic Approach to WLAN User Location Estimation. IJWIN, 9, 155–164. https://doi.org/10.1023/A:1016003126882
  • Xiang, Z., Song, S., Chen, J., Wang, H., Huang, J. ve Gao, X. (2004). A wireless LAN-based indoor positioning technology. IBM Journal of Research and Development, 48(5–6), 617–626. https://doi.org/10.1147/rd.485.0617
  • Feldmann, S., Kyamakya, K., Zapater, A. ve Lue, Z. (2003). An indoor Bluetooth-based positioning system: Concept, implementation and experimental evaluation. Proceedings of the International Conference on Wireless Networks, 109–113 . Tilch, S. ve Mautz, R. (2010). Current investigations at the ETH Zurich in optical indoor positioning. 2010 7th Workshop on Positioning, Navigation and Communication, 174–178. https://doi.org/10.1109/WPNC.2010.5653591
  • Koyuncu, H. ve Yang, S. H. (2010). A Survey of Indoor Positioning and Object Locating Systems. International Journal of Computer Science and Network Security (IJCSNS ’10), 10(5), 121–128.
  • Nguyen, N. H. ve Dogancay, K. (2016). Optimal Geometry Analysis for Multistatic TOA Localization. IEEE Transactions on Signal Processing, 64(16), 4180–4193. https://doi.org/10.1109/TSP.2016.2566611
  • He, J., Geng, Y., Liu, F. ve Xu, C. (2014). CC-KF: Enhanced TOA Performance in Multipath and NLOS Indoor Extreme Environment. IEEE Sensors Journal, 14(11), 3766–3774. https://doi.org/10.1109/JSEN.2014.2328353
  • Malajner, M., Gleich, D. ve Planinsic, P. (2015). Angle of Arrival Measurement Using Multiple Static Monopole Antennas. IEEE Sensors Journal, 15(6), 3328–3337. https://doi.org/10.1109/JSEN.2014.2386537
  • H. W. Griepentrog, B. S. Blackmore ve S. G. Vougioukas (2006). Positioning and Navigation. Mechatronics and Applications, in CIGR Handbook of Agricultural Engineering, Volume VI, 195-204.
  • H. Aydın, B. Erkmen (2019). Kapalı Alan Yaya Konumlandırma Sistemi. Journal of Engineering Sciences and Design. DOI: 10.21923/jesd.450256

Pedometre, GNSS ve IMU ile Mesafe Ölçüm Doğruluğunun Tespit Edilmesi ve Yeni Geliştirilen İvme Sensörü Temelli Yöntem

Yıl 2022, , 138 - 143, 28.06.2022
https://doi.org/10.46460/ijiea.1079781

Öz

Konum belirleme uygulamaları, son yıllarda mobil telefonlarının da konum belirleyebilmesi kabiliyetini edinmesi ile yaygınlaşmaya başlamıştır. Genel olarak GNSS ile konum belirleme yöntemleri kullanılırken, kapalı alanda GNSS sisteminin verimli olarak çalışmaması neticesinde, konum belirleme için başka yöntemler geliştirilmesi çabası içine girilmiştir. Yaygın olarak yerel ağlar kurmak ve bu yerel ağlar ile mesafeler ölçerek konum belirlemek bu yöntemlerin temel çalışma prensibidir. Bunun için, Bluetooth, kablosuz ağlar ve değişik frekanslara sahip sinyaller, ses ötesi sinyaller, radyo frekansları ile çalışan RFID modülleri vb. sistemler kullanılmaktadır. Hem gerçek zamanlı veriler üretilebilmekte hem de üretilen veriler sunuculara kaydedilerek saklanabilmekte ve daha sonra kullanıcılar hizmetine sunulmaktadır. Kullanılan teknolojiye göre yüksek ve orta konum doğruluğuna sahip veriler elde edilebilmektedir. Ancak bu sistemlerin ağ oluşturma gereksinimi yüzünden yüksek maliyetli olması ve açık alanlarda sonuç üretememesi yüzünden özellikle spor uygulamalarında IMU temelli sadece mesafe ölçen çözümlerin oluşturulmasına neden olmuştur. Sunulan makalede, konum belirlemenin temeli olan mesafe ölçümünün yüksek doğrulukta sadece ivme sensörü kullanılarak elde edilmesine yönelik yöntem ve filtreler geliştirilmiştir. Elde edilen sonuçlar, diğer mesafe ölçme yöntemleri ve gerçek mesafe ile kıyaslanmıştır. Geliştirilen filtreler, değişken adım uzunluğu tespiti ile mesafe ölçümü hesabına dayalı olarak, yanlış adım tespitini engellemek, statik hareketleri filtrelemek ve genel olarak ivme sensörünün gürültülerini azaltarak doğru mesafeyi hesaplamak amacıyla geliştirilmiştir. Filtreler kullanılmadan ivme sensörü ile elde edilen mesafede hata miktarı %60’lara ulaşırken, geliştirilen yöntem ile hata miktarı %2’nin altına düşmüştür. Bu sonuçlar, geliştirilen yöntemin mesafe ölçümünde ve metre altı konum belirlemede kullanılabileceğini açıkça göstermiştir.

Kaynakça

  • Hightower, J., Borriello, G. ve Want, R. (2000). SpotON: An Indoor 3D Location Sensing Technology Based on RF Signal Strength. UW CSE00-02
  • Ni, L. M., Yunhao Liu, Yiu Cho Lau ve Patil, A. P. (2003). LANDMARC: indoor location sensing using active RFID. Proceedings of the First IEEE International Conference on Pervasive Computing and Communications, 2003. (PerCom 2003)., 407–415. https://doi.org/10.1109 /PERCOM.2003.1192765
  • Jin, G., Lu, X. ve Park, M.-S. (2006). An Indoor Localization Mechanism Using Active RFID Tag. IEEE International Conference on Sensor Networks, Ubiquitous, and Trustworthy Computing -Vol 1 (SUTC’06), 1, 40–43. https://doi.org/10.1109/SUTC.2006.1636157
  • Bahl, P. ve Padmanabhan, V. N. (2000). RADAR: an in-building RF-based user location and tracking system. Proceedings IEEE INFOCOM 2000. Conference on Computer Communications. Nineteenth Annual Joint Conference of the IEEE Computer and Communications Societies (Cat. No.00CH37064), 2, 775–784. https://doi.org/10.1109/INFCOM.2000.832252
  • Ingram, S. J., Harmer, D. ve Quinlan, M. (2014). UltraWideBand indoor positioning systems and their use in emergencies. PLANS 2004. Position Location and Navigation Symposium (IEEE Cat. No.04CH37556), 706–715. https://doi.org/10.1109/PLANS.2004.1309063
  • Jiang L., Hoe L-N ve Loon L-L. (2010). Integrated UWB and GPS location sensing system in hospital environment. 2010 5th IEEE Conference on Industrial Electronics and Applications, 286–289. https://doi.org/10.1109/ICIEA.2010.5516828
  • Gigl, T., Janssen, G. J. M., Dizdarević, V., Witrisal, K. ve Irahhauten, Z. (2007). Analysis of a UWB indoor positioning system based on received signal strength. 4th Workshop on Positioning, Navigation and Communication 2007, WPNC’07 - Workshop Proceedings, 2007(1), 97–101. https://doi.org/10.1109/wpnc.2007.353618
  • Ni, L. M., Yunhao Liu, Yiu Cho Lau ve Patil, A. P. (2003). LANDMARC: indoor location sensing using active RFID. Proceedings of the First IEEE International Conference on Pervasive Computing and Communications, 2003. (PerCom 2003)., 407–415. https://doi.org/10.1109/PERCOM.2003.1192765
  • Hazas, M. ve Hopper, A. (2006). Broadband ultrasonic location systems for improved indoor positioning. IEEE Transactions on Mobile Computing, 5(5), 536–547. https://doi.org/10.1109/TMC.2006.57
  • Roos, T., Myllymäki, P., Tirri, H., Misikangas, P. ve Sievänen, J. (2002). A Probabilistic Approach to WLAN User Location Estimation. IJWIN, 9, 155–164. https://doi.org/10.1023/A:1016003126882
  • Xiang, Z., Song, S., Chen, J., Wang, H., Huang, J. ve Gao, X. (2004). A wireless LAN-based indoor positioning technology. IBM Journal of Research and Development, 48(5–6), 617–626. https://doi.org/10.1147/rd.485.0617
  • Feldmann, S., Kyamakya, K., Zapater, A. ve Lue, Z. (2003). An indoor Bluetooth-based positioning system: Concept, implementation and experimental evaluation. Proceedings of the International Conference on Wireless Networks, 109–113 . Tilch, S. ve Mautz, R. (2010). Current investigations at the ETH Zurich in optical indoor positioning. 2010 7th Workshop on Positioning, Navigation and Communication, 174–178. https://doi.org/10.1109/WPNC.2010.5653591
  • Koyuncu, H. ve Yang, S. H. (2010). A Survey of Indoor Positioning and Object Locating Systems. International Journal of Computer Science and Network Security (IJCSNS ’10), 10(5), 121–128.
  • Nguyen, N. H. ve Dogancay, K. (2016). Optimal Geometry Analysis for Multistatic TOA Localization. IEEE Transactions on Signal Processing, 64(16), 4180–4193. https://doi.org/10.1109/TSP.2016.2566611
  • He, J., Geng, Y., Liu, F. ve Xu, C. (2014). CC-KF: Enhanced TOA Performance in Multipath and NLOS Indoor Extreme Environment. IEEE Sensors Journal, 14(11), 3766–3774. https://doi.org/10.1109/JSEN.2014.2328353
  • Malajner, M., Gleich, D. ve Planinsic, P. (2015). Angle of Arrival Measurement Using Multiple Static Monopole Antennas. IEEE Sensors Journal, 15(6), 3328–3337. https://doi.org/10.1109/JSEN.2014.2386537
  • H. W. Griepentrog, B. S. Blackmore ve S. G. Vougioukas (2006). Positioning and Navigation. Mechatronics and Applications, in CIGR Handbook of Agricultural Engineering, Volume VI, 195-204.
  • H. Aydın, B. Erkmen (2019). Kapalı Alan Yaya Konumlandırma Sistemi. Journal of Engineering Sciences and Design. DOI: 10.21923/jesd.450256
Toplam 18 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Mühendislik
Bölüm Makaleler
Yazarlar

Uğur Acar 0000-0003-3676-4259

Yayımlanma Tarihi 28 Haziran 2022
Gönderilme Tarihi 28 Şubat 2022
Yayımlandığı Sayı Yıl 2022

Kaynak Göster

APA Acar, U. (2022). Pedometre, GNSS ve IMU ile Mesafe Ölçüm Doğruluğunun Tespit Edilmesi ve Yeni Geliştirilen İvme Sensörü Temelli Yöntem. International Journal of Innovative Engineering Applications, 6(1), 138-143. https://doi.org/10.46460/ijiea.1079781
AMA Acar U. Pedometre, GNSS ve IMU ile Mesafe Ölçüm Doğruluğunun Tespit Edilmesi ve Yeni Geliştirilen İvme Sensörü Temelli Yöntem. ijiea, IJIEA. Haziran 2022;6(1):138-143. doi:10.46460/ijiea.1079781
Chicago Acar, Uğur. “Pedometre, GNSS Ve IMU Ile Mesafe Ölçüm Doğruluğunun Tespit Edilmesi Ve Yeni Geliştirilen İvme Sensörü Temelli Yöntem”. International Journal of Innovative Engineering Applications 6, sy. 1 (Haziran 2022): 138-43. https://doi.org/10.46460/ijiea.1079781.
EndNote Acar U (01 Haziran 2022) Pedometre, GNSS ve IMU ile Mesafe Ölçüm Doğruluğunun Tespit Edilmesi ve Yeni Geliştirilen İvme Sensörü Temelli Yöntem. International Journal of Innovative Engineering Applications 6 1 138–143.
IEEE U. Acar, “Pedometre, GNSS ve IMU ile Mesafe Ölçüm Doğruluğunun Tespit Edilmesi ve Yeni Geliştirilen İvme Sensörü Temelli Yöntem”, ijiea, IJIEA, c. 6, sy. 1, ss. 138–143, 2022, doi: 10.46460/ijiea.1079781.
ISNAD Acar, Uğur. “Pedometre, GNSS Ve IMU Ile Mesafe Ölçüm Doğruluğunun Tespit Edilmesi Ve Yeni Geliştirilen İvme Sensörü Temelli Yöntem”. International Journal of Innovative Engineering Applications 6/1 (Haziran 2022), 138-143. https://doi.org/10.46460/ijiea.1079781.
JAMA Acar U. Pedometre, GNSS ve IMU ile Mesafe Ölçüm Doğruluğunun Tespit Edilmesi ve Yeni Geliştirilen İvme Sensörü Temelli Yöntem. ijiea, IJIEA. 2022;6:138–143.
MLA Acar, Uğur. “Pedometre, GNSS Ve IMU Ile Mesafe Ölçüm Doğruluğunun Tespit Edilmesi Ve Yeni Geliştirilen İvme Sensörü Temelli Yöntem”. International Journal of Innovative Engineering Applications, c. 6, sy. 1, 2022, ss. 138-43, doi:10.46460/ijiea.1079781.
Vancouver Acar U. Pedometre, GNSS ve IMU ile Mesafe Ölçüm Doğruluğunun Tespit Edilmesi ve Yeni Geliştirilen İvme Sensörü Temelli Yöntem. ijiea, IJIEA. 2022;6(1):138-43.