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Entropy Flow-Aided Navigation

Published online by Cambridge University Press:  26 November 2010

He Deng ,
Chao Pan ,
Tongxin Wen  and
Jianguo Liu
He Deng
Affiliation:
(National Key Laboratory of Science and Technology, Huazhong University, Wuhan, PRC)
Chao Pan
Affiliation:
(National Key Laboratory of Science and Technology, Huazhong University, Wuhan, PRC)
Tongxin Wen
Affiliation:
(National Key Laboratory of Science and Technology, Huazhong University, Wuhan, PRC)
Jianguo Liu*
Affiliation:
(National Key Laboratory of Science and Technology, Huazhong University, Wuhan, PRC)
*
(Email: jgliu@ieee.org)
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Abstract

Integrating the visual navigation mechanism of flying insects with a nonlinear Kalman filter, this paper proposes a novel navigation algorithm. New concepts of entropic map and entropy flow are presented, which can characterize topographic features and measure changes of the image respectively. Meanwhile, an auto-selecting algorithm of assessment threshold is proposed to improve computational accuracy and efficiency of global motion estimation. The simulation results suggest that the navigation algorithm can perform real-time rectification of the missile's trajectory well, and can reduce the cost of the missile's hardware.

Keywords

entropy flowoptical flownavigation

Information

Type
Research Article
Information
The Journal of Navigation , Volume 64 , Issue 1 , January 2011 , pp. 109 - 125
Copyright
Copyright © The Royal Institute of Navigation 2010

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References

REFERENCES

[1]Farrell, J. L. (1976). Integrated Aircraft Navigation. New York: Academic.Google Scholar
[2]Enns, R. and Morrell, D. (1995). Terrain-aided navigation using the viterbi algorithm. Journal of Guidance, Control and Dynamics, 18, 1444–1449.Google Scholar
[3]Bertoni, G., Borghi, D. and Zanzi, M. (1998). The GPS integrity problem. Proceeding of the 14 thIFAC Symposium on Automatic Control in Aerospace, Soeul, Korea.Google Scholar
[4]Esch, H. E. and Burns, J. E. (1996). Distance estimation by foraging honeybees. Journal of Experimental Biology, 199, 155–162.CrossRefGoogle ScholarPubMed
[5]Reisenman, C., Haag, J. and Borst, A. (2003). Adaptation of response transients in fly motion vision. I: Experiments. Vision Research, 43, 1291–1307.CrossRefGoogle ScholarPubMed
[6]Collett, T. S., Dillmann, E., Giger, A. and Wehner, R. (1992). Visual landmarks and route following in desert ants. Journal of Comparative Physiology A, 170, 435–442.CrossRefGoogle Scholar
[7]Wehner, R. (2003). Desert ant navigation: how miniature brains solve complex tasks. Journal of Comparative Physiology A, 189, 579–588.CrossRefGoogle ScholarPubMed
[8]Srinivasan, M. V., Zhang, S. W., Lehrer, M. and Collett, T. S. (1996). Honeybee navigation en route to the goal: visual flight control and odometer. Journal of Experimental Biology, 199, 237–244.CrossRefGoogle Scholar
[9]O′Carroll, D. C., Bidwell, N. J., Laughlin, S. B. and Warrant, E. J. (1996). Insect motion detectors matched to visual ecology. Nature, 382, 63–66.CrossRefGoogle ScholarPubMed
[10]Srinivasan, M. V., Lehrer, M., Kirchner, W. H. and Zhang, S. W. (1991). Range perception through apparent image speed in freely flying honeybees. Visual Neuroscience, 6, 519–535.CrossRefGoogle ScholarPubMed
[11]Baird, E., Srinivasan, M. V., Zhang, S., Lamont, R. and Cowling, A. (2006). Visual control of flight speed and height in honeybee. Lecture Notes in Artificial Intelligence, 4095, 40–51.Google Scholar
[12]Franceschini, N., Ruffier, F. and Serres, J. (2007). A bio-inspired flying robot sheds light on insect piloting abilities. Current Biology, 17, 329–335.CrossRefGoogle ScholarPubMed
[13]von Frisch, K. (1967). The dance language and orientation of honeybees. Cambridge: Belknap.Google Scholar
[14]David, C. T. (1982). Compensation for height in the control of groundspeed by Drosophila in a new, “Barber's Pole” wind tunnel. Journal of Comparative Physiology A, 147, 485–493.CrossRefGoogle Scholar
[15]Baird, E., Srinivasan, M. V., Zhang, S. W. and Cowling, A. (2005). Visual control of flight speed in honeybees. Journal of Experimental Biology, 208, 3895–3905.CrossRefGoogle ScholarPubMed
[16]Esch, H. E., Zhang, S. W., Srinivasan, M. V. and Tautz, J. (2001). Honeybee dances communicate distances measured by optic flow. Nature, 411, 581–583.Google Scholar
[17]Shannon, C. E. (1948). A mathematical theory of communication. ACM SIGMOBILE Mobile Computing and Communications Review, 5, 3–55.CrossRefGoogle Scholar
[18]Shiozacki, A. (1986). Edge extraction using entropy operator. Computer Vision, Graphics and Image Processing archive, 36, 1–9.CrossRefGoogle Scholar
[19]Si, A., Srinivasan, M. V. and Zhang, S. (2003). Honeybee navigation: properties of the visually driven ‘odometer’. Journal of Experimental Biology, 206, 1265–1273.CrossRefGoogle ScholarPubMed
[20]Horn, B. and Schunck, B. (1981). Determining optical flow. Artificial Intelligence, 17, 185–203.CrossRefGoogle Scholar
[21]Lucas, B. and Kanade, T. (1981). An iterative image registration technique with an application to stereo vision. Proceedings of the 7 thInternational Joint Conference on Artificial Intelligence, Canada.Google Scholar
[22]Barron, J. L., Fleet, D. J. and Beauchemin, S. S. (1994). Performance of optical flow techniques. International Journal of Computer Vision, 12, 43–77.CrossRefGoogle Scholar
[23]Fleet, D. J. and Jepson, A. D. (1990). Computation of component image velocity from local phase information. International Journal of Computer Vision, 5, 77–104.CrossRefGoogle Scholar
[24]Galvin, B., McCane, B., Novins, K., Mason, D. and Mills, S. (1998). Recovering motion fields: an analysis of eight optical flow algorithms. Proceedings of 1998 British Machine Vision Conference, Southampton, England.Google Scholar
[25]Beauchemin, S. S. and Barron, J. L. (1995). The computation of optical flow. ACM Computing Surveys, 27, 433–467.Google Scholar
[26]Kalman, R. E. (1960). A new approach to linear filtering and prediction problems. Transactions of the ASME – Journal of Basic Engineering, 82, 35–45.Google Scholar
[27]Pitman, G. R. (1962). Inertial Guidance. New York: Academic.Google Scholar