Araştırma Makalesi
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Navigation Under GNSS Denied Environments: Zero Velocity and Zero Turning Update

Yıl 2022, Sayı: 38, 360 - 369, 31.08.2022
https://doi.org/10.31590/ejosat.1090813

Öz

The objective of this paper is to present a method which bounds the error of an inertial navigation system (INS) when Global Navigation Satellite System (GNSS) is not available. Inertial navigation systems utilize gyroscopes and accelerometers, and calculate velocity, position and attitude, essentially by integrating the measurements obtained from these sensors. Due to the nature of integration, INS are notoriously prone to sensor biases and drifts. Typically, GNSS is used to correct the navigation system errors caused by the inertial sensor measurements. However, in GNSS degraded or denied environments, alternative solutions are required. If the platform on which an INS is mounted is known or estimated to be stationary, zero-velocity update (ZUPT) and/or zero turning update (ZTUPT) algorithms can be applied in order to bound the navigation system errors. Under certain assumptions, ZUPT based algorithms can be applied when the platform is not stationary. If a vehicle’s motion is constrained by the design of its kinematics, i.e. if it can be assumed that the vehicle cannot move or rotate along one or more of its body axes, ZUPT assisted Kalman estimators can be used to correct the errors along those axes. Potentially, ZUPT based estimation algorithms can also be utilized when a sufficiently high fidelity vehicle model is available. In this paper, the implementation of zero-velocity update (ZUPT) and zero turning update (ZTUPT) algorithms are analyzed for the purpose of estimating and bounding inertial navigation errors. The basic principle in navigation is based on combining the data obtained from the sensors onboard and the inertial navigation system through an Extended Kalman filter. Although this process requires additional software components, it potentially offers increased system accuracy and reliability. Incorporating the kinematics of the vehicle, along with a ZUPT and/or ZTUPT algorithm, provides additional data to feed into the Kalman filter and increases the efficiency of error estimation. Estimated error is then fed back into the INS algorithm in order to counteract the sources of error.

Kaynakça

  • Akcayir, Y. and Ozkazanc, Y. (2003). Gyroscope drift estimation analysis in land navigation systems. In IEEE Conference on Control Applications, volume 2, pages 1488–1491. doi: 10.1109/cca.2003.1223234.
  • Goshen-Meskin, D. and Bar-Itzhack, I. Y. (1992a). Observability analysis of piece-wise constant systems. i. theory. IEEE Transactions on Aerospace and Electronic Systems, 28(4):1056–1067. doi: 10.1109/7.165367.
  • Goshen-Meskin, D. and Bar-Itzhack, I. Y. (1992b). Observability analysis of piece-wise constant systems. ii. application to inertial navigation in-flight alignment (military applications). IEEE Transactions on Aerospace and Electronic Systems, 28(4):1068–1075. doi: 10.1109/7.165368.
  • Groves, Paul D. (2013). Principles of GNSS, inertial, and multisensor integrated navigation systems. Artech House, second edition.
  • Julier, S. J. and Uhlmann, J. K. (2004). Unscented filtering and nonlinear estimation. Proceedings of the IEEE, 92(3):401–422. doi: 10.1109/jproc.2003.823141.
  • Ma, H., Yan, L., Xia, Y., and Fu, M. (2020). Kalman filtering and information fusion. Science Press.
  • Musoff, H. and Zarchan, P. (2009). Fundamentals of kalman filtering: a practical approach. The American Institute of Aeronautics and Astronautics, 3rd edition.
  • Schmidt, G. T. (2015). Navigation sensors and systems in GNSS degraded and denied environments. Chinese Journal of Aeronautics, 28:1–10. doi: 10.23919/icins.2018.8405890.
  • Titterton, D. and Weston, J. (2004). Strapdown inertial navigation technology. The American Institute of Aeronautics and Astronautics, 2nd edition.
  • Wagstaff, B. and Kelly, J. (2018). LSTM-Based zero-velocity detection for robust inertial navigation. In International Conference on Indoor Positioning and Indoor Navigation, pages 1–8. doi: 10.1109/ipin.2018.8533770.
  • Wahlström, J., Skog, I., Gustafsson, F., Markham, A., and Trigoni, N. (2019). Zero-velocity detection - a bayesian approach to adaptive thresholding. IEEE Sensors Letters, 3(6):1–4. doi: 10.1109/lsens.2019.2917055.
  • Wan, E. (2006). Sigma-point filters: an overview with applications to integrated navigation and vision assisted control. In IEEE Nonlinear Statistical Signal Processing Workshop, pages 201–202. doi: 10.1109/nsspw.2006.4378854.
  • Xiaofang, L., Yuliang, M., Ling, X., Jiabin, C., and Chunlei, S. (2014). Applications of zero-velocity detector and Kalman filter in zero velocity update for inertial navigation system. In IEEE Chinese Guidance, Navigation and Control Conference, pages 1760–1763. doi: 10.1109/cgncc.2014.7007449.

Küresel Konumlama Sisteminin Olmadığı Ortamlarda Navigasyon: Sıfır Hız ve Sıfır Dönü Güncelleme

Yıl 2022, Sayı: 38, 360 - 369, 31.08.2022
https://doi.org/10.31590/ejosat.1090813

Öz

Bu makalenin amacı, Küresel Konumlama Sisteminin (KKS) mevcut olmadığı durumlarda, bir ataletsel navigasyon sisteminin hata sinyallerini sınırlandırmayı amaçlayan bir yöntem sunmaktır. Ataletsel navigasyon sistemleri (ANS) dönüölçer ve ivmeölçerleri kullanır ve –özde– bu algılayıcılardan elde edilen sinyallerinin integralini alınarak hız, konum ve yönelimi hesaplar. Integral alma işleminin doğası gereği ANS, algılayıcıların kaymalarına ve sapmalarına karşı son derece hassastır. Tipik olarak, KKS, ataletsel algılayıcı ölçümlerinin neden olduğu navigasyon sistemi hatalarını düzeltmek için kullanılır. Ancak, KKS’nin kalitesinin bozulduğu veya KKS verisinin erişilir olmadığı ortamlarda alternatif çözümler gereklidir. Eğer bir ANS’nin monte edildiği platformun hareketsiz olduğu biliniyor veya tahmin ediliyor ise, navigasyon sistemi hatalarını sınırlandırmak amacıyla, sıfır hız güncellemesi (ZUPT) ve sıfır dönü güncellemesi (ZTUPT) algoritmaları uygulanabilir. Belirli varsayımlar altında, platformun durağan olmadığı durumlarda da ZUPT tabanlı algoritmalar uygulanabilir. Eğer bir aracın hareketi, kinematiğinin tasarımı ile sınırlıysa, yani aracın hareket edemeyeceği veya dönemeyeceği bir veya daha fazla eksen varsa, ZUPT destekli Kalman filtre algoritmaları bu eksenler doğrultusundaki hataları düzeltmek için kullanılabilir. Potansiyel olarak, ZUPT tabanlı tahmin algoritmaları, yeterince yüksek sadakatli bir araç modeli mevcutsa da kullanılabilir. Bu makalede, sıfır hız güncellemesi (ZUPT) ve/veya sıfır dönü güncellemesinin uygulanması yoluyla ataletsel navigasyon sistemi hatalarının tahmin edilmesi ve sınırlandırılması konusu incelenmektedir. Navigasyondaki temel prensip, platform üzerindeki algılayıcılardan elde edilen verilerin bir Genişletilmiş Kalman filtresi aracılığıyla ataletsel navigasyon sistemine entegre edilmesine dayanır. Bu işlem ek yazılım bileşenleri gerektirse de, potansiyel olarak artan bir doğruluk ve güvenilirlik sunar. Sıfır hız ve sıfır dönü algoritmalarına araç kinematiklerinin de eklemlenmesi, Kalman filtreye ek veri sağlar ve hata tahmininin doğruluğunu artırır. Tahmin edilen hata ANS algoritmasına geri beslenerek hata kaynaklarının etkisinin azaltılması sağlanır.

Kaynakça

  • Akcayir, Y. and Ozkazanc, Y. (2003). Gyroscope drift estimation analysis in land navigation systems. In IEEE Conference on Control Applications, volume 2, pages 1488–1491. doi: 10.1109/cca.2003.1223234.
  • Goshen-Meskin, D. and Bar-Itzhack, I. Y. (1992a). Observability analysis of piece-wise constant systems. i. theory. IEEE Transactions on Aerospace and Electronic Systems, 28(4):1056–1067. doi: 10.1109/7.165367.
  • Goshen-Meskin, D. and Bar-Itzhack, I. Y. (1992b). Observability analysis of piece-wise constant systems. ii. application to inertial navigation in-flight alignment (military applications). IEEE Transactions on Aerospace and Electronic Systems, 28(4):1068–1075. doi: 10.1109/7.165368.
  • Groves, Paul D. (2013). Principles of GNSS, inertial, and multisensor integrated navigation systems. Artech House, second edition.
  • Julier, S. J. and Uhlmann, J. K. (2004). Unscented filtering and nonlinear estimation. Proceedings of the IEEE, 92(3):401–422. doi: 10.1109/jproc.2003.823141.
  • Ma, H., Yan, L., Xia, Y., and Fu, M. (2020). Kalman filtering and information fusion. Science Press.
  • Musoff, H. and Zarchan, P. (2009). Fundamentals of kalman filtering: a practical approach. The American Institute of Aeronautics and Astronautics, 3rd edition.
  • Schmidt, G. T. (2015). Navigation sensors and systems in GNSS degraded and denied environments. Chinese Journal of Aeronautics, 28:1–10. doi: 10.23919/icins.2018.8405890.
  • Titterton, D. and Weston, J. (2004). Strapdown inertial navigation technology. The American Institute of Aeronautics and Astronautics, 2nd edition.
  • Wagstaff, B. and Kelly, J. (2018). LSTM-Based zero-velocity detection for robust inertial navigation. In International Conference on Indoor Positioning and Indoor Navigation, pages 1–8. doi: 10.1109/ipin.2018.8533770.
  • Wahlström, J., Skog, I., Gustafsson, F., Markham, A., and Trigoni, N. (2019). Zero-velocity detection - a bayesian approach to adaptive thresholding. IEEE Sensors Letters, 3(6):1–4. doi: 10.1109/lsens.2019.2917055.
  • Wan, E. (2006). Sigma-point filters: an overview with applications to integrated navigation and vision assisted control. In IEEE Nonlinear Statistical Signal Processing Workshop, pages 201–202. doi: 10.1109/nsspw.2006.4378854.
  • Xiaofang, L., Yuliang, M., Ling, X., Jiabin, C., and Chunlei, S. (2014). Applications of zero-velocity detector and Kalman filter in zero velocity update for inertial navigation system. In IEEE Chinese Guidance, Navigation and Control Conference, pages 1760–1763. doi: 10.1109/cgncc.2014.7007449.
Toplam 13 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Mühendislik
Bölüm Makaleler
Yazarlar

Oğuzhan Çifdalöz 0000-0003-0523-946X

Erken Görünüm Tarihi 26 Temmuz 2022
Yayımlanma Tarihi 31 Ağustos 2022
Yayımlandığı Sayı Yıl 2022 Sayı: 38

Kaynak Göster

APA Çifdalöz, O. (2022). Navigation Under GNSS Denied Environments: Zero Velocity and Zero Turning Update. Avrupa Bilim Ve Teknoloji Dergisi(38), 360-369. https://doi.org/10.31590/ejosat.1090813