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Estimation of Visual Shoreline Navigation Errors

Published online by Cambridge University Press:  16 November 2018

Vladimir A. Grishin
Vladimir A. Grishin*
Affiliation:
(Space Research Institute of the Russian Academy of Sciences (RAS), Moscow, Russia) (Bauman Moscow State Technical University, Moscow, Russia)
*
(E-mail: [email protected])
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Abstract

In some cases, navigation of aircraft or spacecraft may need to be conducted in a Global Navigation Satellite System (GNSS)-denied environment. So, additional sources of navigation information may need to be used to increase navigation precision and resilience. Such sources can include visual navigation systems such as visual shoreline navigation. The main feature of visual shoreline navigation is the severe variability of navigation errors depending on the shape of the observed shoreline, the distance and the view angle of the observation. Such variations are so great that it is not possible to use average values of errors. So, each measurement of an aircraft or spacecraft position should be accompanied with an estimation of the error covariance matrix in real time. It is proposed to use the Cramer-Rao lower bound of visual shoreline navigation errors as such a matrix. The method for constructing the Cramer-Rao lower bound is described in this paper.

Keywords

Visual shoreline navigationLower bounds estimation of error covariance matrix
Type
Research Article
Information
The Journal of Navigation , Volume 72 , Issue 2 , March 2019 , pp. 389 - 404
Copyright
Copyright © The Royal Institute of Navigation 2018 

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References

REFERENCES

Aksakal, S. (2013). Geometric Accuracy Investigations of SEVIRI High Resolution Visible (HRV) Level 1·5 Imagery. Remote Sensing, 5, 2475–2491.Google Scholar
Baldina, E.A., Bessonov, R.V., Grishin, V.A., Zhukov, B.S. and Kharkovets, E.G. (2016). Suitability estimation of GSHHG coast line map for autonomous optical spacecraft navigation. Modern problems of remote sensing of the Earth from space, 3, 217–228. (In Russian) http://d33.infospace.ru/d33_conf/sb2016t3/217-228.pdf. Accessed 14 April 2018.Google Scholar
Carr, J. and Madani, H. (2007). Measuring Image Navigation and Registration Performance at the 3-σ Level Using Platinum Quality Landmarks. Proceedings of the 20th International Symposium on Space Flight Dynamics (ISSFD), Annapolis, Maryland, USA.Google Scholar
Fujii, K. and Arakawa, K. (2004). Automatic Registration of Satellite Image to Map in Urban Area. Theory and Applications of GIS, 1, 15–22.Google Scholar
Global Self-consistent, Hierarchical, High-resolution Geography Database (GSHHG). (2017). http://www.soest.hawaii.edu/pwessel/gshhg/. Accessed 14 April 2018.Google Scholar
Habbecke, M. and Kobbelt, L. (2012). Automatic Registration of Oblique Aerial Images with Cadastral Maps. Trends and Topics in Computer Vision, Volume 6554 of the series Lecture Notes in Computer Science. Kutulakos, Kiriakos N. (ed.), 253–266.Google Scholar
Li, Y. and Briggs, R. (2006). Automated Georeferencing Based on Topological Point Pattern Matching. Proceedings of the International Symposium on Automated Cartography (AutoCarto'06), Vancouver.Google Scholar
Li, Y.-H. and Yeh, P.-C. (2012). An Interpretation of the Moore-Penrose Generalized Inverse of a Singular Fisher Information Matrix. IEEE Transactions on Signal Processing, 10, 5532–5536.Google Scholar
Madani, H., Carr, J. L. and Shoeser, C. (2004). Image registration using autolandmark. Proceedings of 2004 IEEE International Geoscience and Remote Sensing Symposium, USA, Vol. 6, 3778–3781.Google Scholar
Van Trees, H. L. (2001). Detection, Estimation, and Modulation Theory, Part 1: Detection, Estimation, and Linear Modulation Theory. John Wiley & Sons, Inc., New York.Google Scholar
Wang, C., Stefanidis, A., Croitoru, A. and Agouris, P. (2008). Map Registration of Image Sequences Using Linear Features. Photogrammetric Engineering & Remote Sensing, 1, 25–38.Google Scholar
Wessel, P. and Smith, W. H. F. (1996). A global, self-consistent, hierarchical, high-resolution shoreline database. Journal of Geophysical Research, B4, 8741–8743.Google Scholar
Zeng, D.[Dan], Fang, R.[Rui], Ge, S.M.[Shi-Ming], Li, S.Y.[Shu-Ying] and Zhang, Z.J.[Zhi-Jiang] (2017). Geometry-Based Global Alignment for GSMS Remote Sensing Images. Remote Sensing, 9, 587.Google Scholar