Abstract
Günümüz teknolojik gelişmeleri, konum belirleme sistemlerinin mühendislik uygulamaları ve disiplinler arası alanlardaki kritik rolünü vurgulamaktadır. Light Detection and Ranging (LiDAR), Inertial Measurement Unit (IMU) ve Global Navigation Satellite System (GNSS) tabanlı sistemler, geomatik mühendisliği, akıllı şehir uygulamaları, insansız hava araçları, otonom araçlar ve robotik sistemler gibi alanlarda yaygın olarak kullanılmaktadır. Ancak, yüksek hassasiyet gereksinimleri ve bütçe kısıtları, düşük maliyetli sistemlerin yeterli doğrulukla veri sağlayamaması nedeniyle kullanımını zorlaştırmaktadır. Bu bağlamda, GNSS ve IMU verilerinin entegrasyonu, maliyet etkin bir çözüm sunarken doğruluğun artırılmasına katkı sağlamaktadır. Bu çalışma, IPhone 13 Pro cihazından elde edilen GNSS ve IMU verilerinin entegrasyonu ile İstanbul’un Bebek semtinde yaklaşık 330 metrelik bir yürüyüş rotası boyunca bir yol haritası oluşturulmasını incelemiştir. MATLAB ortamında gerçekleştirilen analizde, önce Butterworth ve Kalman filtreleme teknikleri uygulanarak veriler işlenmiş, GNSS verilerine %95, IMU verilerine %5 ağırlık verilerek sapmalar minimize edilmiştir. Kalman filtresinin parametreleri optimize edilerek daha doğru sonuçlar elde edilmiştir. Daha sonra, GNSS ağırlığı %98’e çıkarılıp IMU ağırlığı %2’ye düşürülerek farklı bir senaryo uygulanmış, böylece entegre verilerin doğruluğu artırılmıştır. Bu süreçte, GNSS verilerinin yüksek hassasiyeti ile IMU’nun sinyal kaybı durumlarındaki katkısı optimize edilmiştir. Çalışma, düşük maliyetli cihazlardan elde edilen GNSS ve IMU verilerinin uygun algoritmalarla işlenmesiyle yüksek doğrulukta konum belirleme sağlanabileceğini göstermektedir. Ayrıca, bu çalışma IPhone 13 Pro ile elde edilen GNSS ve IMU verilerinin birleştirilmesi açısından ilk defa uygulanmasının yanı sıra, IPhone 13 Pro GNSS verilerine herhangi bir ek anten ya da post-process işlemi ile doğruluk artırma çalışması yapılmadan iki verinin birleştirilmesi açısından literatüre yenilikler katmaktadır. Bu yöntem, düşük bütçeli projeler için maliyet etkin çözümler sunarak mühendislik projelerine önemli bir katkı sağlamaktadır.
References
- Kilroy, B. (1999). Two Decades of Development and Evaluation of GPS Technology for Natural Resource Applications.
- Sullivan, M. (2012). A Brief History of GPS. PCWorld.
- Charles, D. (2007). GPS Goes Mainstream. NPR, Technology Section, https://www.npr.org/templates/story/story.php?storyId=17611103
- Van Diggelen, F. (2009). WIRELESS-The Smartphone Revolution-Seven Technologies That Put GPS in Mobile Phones Around the World—The How and Why of Location's Entry into Modern Consumer Mobile Communications. GPS World, 20(12), 36.
- Banville, S., & Van Diggelen, F. (2016). Precision GNSS for Everyone. GPS World, 27(11), 43-48.
- Merry, K., & Bettinger, P. (2019). Smartphone GPS Accuracy Study in an Urban Environment. PLoS One, 14(7), e0219890.
- Robustelli, U., Baiocchi, V., & Pugliano, G. (2019). Assessment of Dual Frequency GNSS Observations from a Xiaomi Mi 8 Android Smartphone and Positioning Performance Analysis. Electronics, 8(1), 91.
- Robustelli, U., Paziewski, J., & Pugliano, G. (2021). Observation Quality Assessment and Performance of GNSS Standalone Positioning with Code Pseudoranges of Dual-frequency Android Smartphones. Sensors, 21(6), 2125.
- Zhao, H., & Wang, Z.Y. (2012). Motion Measurement Using Inertial Sensors, Ultrasonic Sensors, and Magnetometers with Extended Kalman Filter for Data Fusion. IEEE Sensors Journal, 12(5), 943-953.
- Tanenhaus, M., Carhoun, D., Geis, T., Wan, E., & Holland, A. (2012). Miniature IMU/INS with Optimally Fused Low Drift MEMS Gyro and Accelerometers for Applications in GPS-denied Environments. In Proceedings of IEEE/ION PLANS 2012, Myrtle Beach, South Carolina (pp. 259-264).
- Zhu, R., & Zhou, Z. (2004). A Real-time Articulated Human Motion Tracking Using Tri-axis Inertial/Magnetic Sensors Package. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 12(2), 295-302.
- Ahmad, N., Ghazilla, R.A.R., Khairi, N.M., & Kasi, V. (2013). Reviews on Various Inertial Measurement Unit (IMU) Sensor Applications. International Journal of Signal Processing Systems, 1(2), 256-262.
- Chu, T., & Akos, D. (2011). Assisted GNSS—Performance Results of Multiplexed Measurements, Limited Bandwidth, and a Vectorized Implementation. In Proceedings of the 2011 International Technical Meeting of the Institute of Navigation, San Diego, CA, USA (pp. 1007-1018).
- Park, M., & Gao, Y. (2008). Error and Performance Analysis of MEMS-based Inertial Sensors with a Low-cost GPS Receiver. Sensors, 9, 2240-2261.
- Godha, S., & Cannon, M.E. (2007). GPS/MEMS INS Integrated System for Navigation in Urban Areas. GPS Solutions, 11, 193-203.
- Bijker, J., & Steyn, W. (2008). Kalman Filter Configurations for a Low-cost Loosely Integrated Inertial Navigation System on an Airship. Control Engineering Practice, 16, 1509-1518.
- Chu, T., Guo, N., Backén, S., & Akos, D. (2012). Monocular Camera/IMU/GNSS Integration for Ground Vehicle Navigation in Challenging GNSS Environments. Sensors, 12(3), 3162-3185.
- Tamimi, R. (2022). Relative Accuracy Found within iPhone Data Collection. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 43, 303-308.
- Cui, Y., & Ge, S.S. (2003). Autonomous Vehicle Positioning with GPS in Urban Canyon Environments. IEEE Transactions on Robotics and Automation, 19(1), 15-25.
- Jafarnia-Jahromi, A., Broumandan, A., Nielsen, J., & Lachapelle, G. (2012). GPS Vulnerability to Spoofing Threats and a Review of Antispoofing Techniques. International Journal of Navigation and Observation, Article ID 127072.
- Seo, J., Walter, T., Marks, E., Chiou, T.Y., & Enge, P. (2007). Ionospheric Scintillation Effects on GPS Receivers during Solar Minimum and Maximum. International Beacon Satellite Symposium 2007, Boston, MA (pp. 11-15).
- Shin, Y., Lee, C., Kim, E., & Walter, T. (2021). Adopting Neural Networks in GNSS-IMU Integration: A Preliminary Study. In 2021 IEEE/AIAA 40th Digital Avionics Systems Conference (DASC) (pp. 1-7). IEEE.
- Sandru, F.D., Nanu, S., Silea, I., & Miclea, R.C. (2016). Kalman and Butterworth Filtering for GNSS/INS Data. In 2016 12th IEEE International Symposium on Electronics and Telecommunications (ISETC) (pp. 257-260). IEEE.
- Butterworth, S. (1930). On the Theory of Filter Amplifiers. Experimental Wireless and Wireless Engineering, 7, 536-541.
- Erer, K.S. (2007). Adaptive Usage of the Butterworth Digital Filter. Journal of Biomechanics, 40(13), 2934-2943.
- Shouran, M., & Elgamli, E. (2020). Design and Implementation of Butterworth Filter. International Journal of Innovative Research in Science Engineering and Technology, 9(9), 7975-7983.
- Chui, C.K., & Chen, G. (2017). Kalman Filtering: With Real-time Applications. Springer.
- Shrotriya, A. (2010). Robot Path Planning and Tracking of a Moving Target Using Kalman Filter (Doctoral dissertation). California State University.
- İlvan, A., & Bostancı, B. (2021). İnsansız Hava Araçları İçin Düşük Bütçeli INS/GNSS Sistemi Entegrasyonunda Genişletilmiş Kalman Filtresi ve Kokusuz Kalman Filtresi Yöntemlerinin Karşılaştırılması.
- İnce, C.D. (1999). Dinamik Sistemlerin GPS ve Kalman Filtresi ile Anlık Olarak İzlenmesi (Doctoral dissertation). İstanbul Teknik Üniversitesi, İstanbul.
Abstract
Today's technological advancements highlight the critical role of positioning systems in engineering applications and interdisciplinary fields. Light Detection and Ranging (LiDAR), Inertial Measurement Unit (IMU), and Global Navigation Satellite System (GNSS)-based systems are widely used in areas such as geomatics engineering, smart city applications, unmanned aerial vehicles, autonomous vehicles, and robotic systems. However, high accuracy requirements and budget constraints make the use of low-cost systems challenging. In this context, the integration of GNSS and IMU data provides a cost-effective solution while contributing to the improvement of positioning accuracy. This study examines the creation of a route map using the integration of GNSS and IMU data obtained from an IPhone 13 Pro device during a walking route of approximately 330 meters in Istanbul's Bebek district. In the MATLAB environment, data were first processed using Butterworth and Kalman filtering techniques, with weights assigned as 95% to GNSS data and 5% to IMU data, minimizing deviations. The parameters of the Kalman filter were optimized to achieve more accurate results. Subsequently, a different scenario was applied by increasing the GNSS weight to 98% and decreasing the IMU weight to 2%, thereby enhancing the accuracy of the integrated data. This process optimized the high accuracy of GNSS data and the contribution of IMU data during signal loss situations. The study demonstrates that high-accuracy positioning can be achieved by processing GNSS and IMU data obtained from low-cost devices with appropriate algorithms. Moreover, this study contributes to the literature by being the first to combine GNSS and IMU data obtained from the IPhone 13 Pro. Additionally, it introduces innovations by integrating the two datasets without any external antenna or post-processing applied to enhance the accuracy of the IPhone 13 Pro GNSS data. This method offers cost-effective solutions for low-budget projects, providing significant contributions to engineering applications.
References
- Kilroy, B. (1999). Two Decades of Development and Evaluation of GPS Technology for Natural Resource Applications.
- Sullivan, M. (2012). A Brief History of GPS. PCWorld.
- Charles, D. (2007). GPS Goes Mainstream. NPR, Technology Section, https://www.npr.org/templates/story/story.php?storyId=17611103
- Van Diggelen, F. (2009). WIRELESS-The Smartphone Revolution-Seven Technologies That Put GPS in Mobile Phones Around the World—The How and Why of Location's Entry into Modern Consumer Mobile Communications. GPS World, 20(12), 36.
- Banville, S., & Van Diggelen, F. (2016). Precision GNSS for Everyone. GPS World, 27(11), 43-48.
- Merry, K., & Bettinger, P. (2019). Smartphone GPS Accuracy Study in an Urban Environment. PLoS One, 14(7), e0219890.
- Robustelli, U., Baiocchi, V., & Pugliano, G. (2019). Assessment of Dual Frequency GNSS Observations from a Xiaomi Mi 8 Android Smartphone and Positioning Performance Analysis. Electronics, 8(1), 91.
- Robustelli, U., Paziewski, J., & Pugliano, G. (2021). Observation Quality Assessment and Performance of GNSS Standalone Positioning with Code Pseudoranges of Dual-frequency Android Smartphones. Sensors, 21(6), 2125.
- Zhao, H., & Wang, Z.Y. (2012). Motion Measurement Using Inertial Sensors, Ultrasonic Sensors, and Magnetometers with Extended Kalman Filter for Data Fusion. IEEE Sensors Journal, 12(5), 943-953.
- Tanenhaus, M., Carhoun, D., Geis, T., Wan, E., & Holland, A. (2012). Miniature IMU/INS with Optimally Fused Low Drift MEMS Gyro and Accelerometers for Applications in GPS-denied Environments. In Proceedings of IEEE/ION PLANS 2012, Myrtle Beach, South Carolina (pp. 259-264).
- Zhu, R., & Zhou, Z. (2004). A Real-time Articulated Human Motion Tracking Using Tri-axis Inertial/Magnetic Sensors Package. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 12(2), 295-302.
- Ahmad, N., Ghazilla, R.A.R., Khairi, N.M., & Kasi, V. (2013). Reviews on Various Inertial Measurement Unit (IMU) Sensor Applications. International Journal of Signal Processing Systems, 1(2), 256-262.
- Chu, T., & Akos, D. (2011). Assisted GNSS—Performance Results of Multiplexed Measurements, Limited Bandwidth, and a Vectorized Implementation. In Proceedings of the 2011 International Technical Meeting of the Institute of Navigation, San Diego, CA, USA (pp. 1007-1018).
- Park, M., & Gao, Y. (2008). Error and Performance Analysis of MEMS-based Inertial Sensors with a Low-cost GPS Receiver. Sensors, 9, 2240-2261.
- Godha, S., & Cannon, M.E. (2007). GPS/MEMS INS Integrated System for Navigation in Urban Areas. GPS Solutions, 11, 193-203.
- Bijker, J., & Steyn, W. (2008). Kalman Filter Configurations for a Low-cost Loosely Integrated Inertial Navigation System on an Airship. Control Engineering Practice, 16, 1509-1518.
- Chu, T., Guo, N., Backén, S., & Akos, D. (2012). Monocular Camera/IMU/GNSS Integration for Ground Vehicle Navigation in Challenging GNSS Environments. Sensors, 12(3), 3162-3185.
- Tamimi, R. (2022). Relative Accuracy Found within iPhone Data Collection. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 43, 303-308.
- Cui, Y., & Ge, S.S. (2003). Autonomous Vehicle Positioning with GPS in Urban Canyon Environments. IEEE Transactions on Robotics and Automation, 19(1), 15-25.
- Jafarnia-Jahromi, A., Broumandan, A., Nielsen, J., & Lachapelle, G. (2012). GPS Vulnerability to Spoofing Threats and a Review of Antispoofing Techniques. International Journal of Navigation and Observation, Article ID 127072.
- Seo, J., Walter, T., Marks, E., Chiou, T.Y., & Enge, P. (2007). Ionospheric Scintillation Effects on GPS Receivers during Solar Minimum and Maximum. International Beacon Satellite Symposium 2007, Boston, MA (pp. 11-15).
- Shin, Y., Lee, C., Kim, E., & Walter, T. (2021). Adopting Neural Networks in GNSS-IMU Integration: A Preliminary Study. In 2021 IEEE/AIAA 40th Digital Avionics Systems Conference (DASC) (pp. 1-7). IEEE.
- Sandru, F.D., Nanu, S., Silea, I., & Miclea, R.C. (2016). Kalman and Butterworth Filtering for GNSS/INS Data. In 2016 12th IEEE International Symposium on Electronics and Telecommunications (ISETC) (pp. 257-260). IEEE.
- Butterworth, S. (1930). On the Theory of Filter Amplifiers. Experimental Wireless and Wireless Engineering, 7, 536-541.
- Erer, K.S. (2007). Adaptive Usage of the Butterworth Digital Filter. Journal of Biomechanics, 40(13), 2934-2943.
- Shouran, M., & Elgamli, E. (2020). Design and Implementation of Butterworth Filter. International Journal of Innovative Research in Science Engineering and Technology, 9(9), 7975-7983.
- Chui, C.K., & Chen, G. (2017). Kalman Filtering: With Real-time Applications. Springer.
- Shrotriya, A. (2010). Robot Path Planning and Tracking of a Moving Target Using Kalman Filter (Doctoral dissertation). California State University.
- İlvan, A., & Bostancı, B. (2021). İnsansız Hava Araçları İçin Düşük Bütçeli INS/GNSS Sistemi Entegrasyonunda Genişletilmiş Kalman Filtresi ve Kokusuz Kalman Filtresi Yöntemlerinin Karşılaştırılması.
- İnce, C.D. (1999). Dinamik Sistemlerin GPS ve Kalman Filtresi ile Anlık Olarak İzlenmesi (Doctoral dissertation). İstanbul Teknik Üniversitesi, İstanbul.
Details
| Primary Language | Turkish |
|---|---|
| Subjects | Information Systems Organisation and Management, Satisfiability and Optimisation |
| Journal Section | Research Articles |
| Authors | |
| Early Pub Date | June 16, 2025 |
| Publication Date | |
| Submission Date | January 17, 2025 |
| Acceptance Date | May 23, 2025 |
| Published in Issue | Year 2025 Volume: 37 Issue: 2 |
