Abstract
Within the last decade, Unmanned aerial vehicles (UAV), for a wide variety of applications have enjoyed growing interest. This paper provides a comprehensive overview of the requirements of the medium-range UAV for target acquisition and surveillance. Special emphasis is given to maximizing the target acquisition with application of fuzzy logic. In order to keep the ground target in the line of sight of the camera and to achieve an optimised target acquisition, this paper presents some novel algorithms and system architecture of aerial vehicle (UAV) that has been ground tested to acquire target location using associativity of its coordinate axes.
The moment UAV is closer to the target, the base station is informed about its coordinates via GPS and then fuzzy logic is employed to calculate its degree of associativity with the target coordinates. Based on the calculation done by the base station, the Inertial Navigation System (INS) of UAV is commanded for camera control and an optimised surveillance is achieved.
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References
Desbiens, A., Hodouin, D., Plamondon, E.: Global predictive control: A unified control structure for decoupling setpoint tracking, Feed forward compensation and disturbance rejection dynamics. In: IEE Proceedings: Control Theory and Applications, vol. 147, pp. 465–475 (2000)
Hoiid, R.: Uimanned Aerial Vehiclra. SMI Publishing, London (1999)
Rysdyk, R.: UAV path following for constant line-of-sight. In: 2nd AIAA Unmanned Unlimited Systems, Technologies, and Operations Aerospace, Land, and Sea Conference, San-Diego, CA, Paper#6626 (September 2003)
Rysdyk, R.: UAV Path Following for Target Observation in Wind. University of Washington, Seattle, WA98195 (2006)
Bardossy, A., Duckstein, L.: Fuzzy rule based modeling with applications to geophysical, biological and engineering systems. CRC press, Inc., Boca Raton (1995)
Mujumdar, P.P., Vedula, S.: Performance evaluation of an irrigation system under some optimal operating policies. Hydrological Sciences – Journal 37(1) (February 1992)
Kosko, B.: Fuzzu thinking: The new science of fuzzy logic, Hyperion, New York (1993)
Zimmermann, H.J.: Fuzzy set theory – and its application. Kluwer Academic, Dordrecht (1991)
MATLAB, Fuzzy Logic Toolbox, The Mathworks, USA (2007b)
Ross, T.J.: Fuzzy Logic with Engineering Applications, McGraw-Hill, Inc. (1997)
Mehta, R., Jain, S.K., Kumar, V.: Fuzzy Technique for Reservoir Operation – effect of Membership functions with different number of categories. Hydrology Journal 28(3-4) (2005)
Mehta, R., Jain, S.K.: Optimal Operation of a Multi-Purpose Reservoir using Neuro-Fuzzy Technique. Water Resour. Manage 23, 509–529 (2009)
Roger, J.J.-S.: Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence, pp. 335–367. Prentice Hall of India (1996)
Klir George, J., Bo, Y.: Fuzzy Sets and Fuzzy Logic. Prentice Hall of India (2008)
Zadeh, L.A.: Outline of a new approach to the analysis of complex system and decision processes. IEEE Transactions on Systems, Man, and Cybernetics SMC-3(1) (1973)
Schulz, K., Huwe, B.: Uncertainty and sensitivity analysis of water transport modeling in a layered soil profile using fuzzy set theory. J. Hydroinf. 1(2), 127–138 (1999)
Nayak, P.C., Sudheer, K.P.: Fuzzy model identification based on cluster estimation for reservoir inflow forecasting, Hydrological Processes. Wiley InterScience (2007), doi: 10.1002/hyp.6644
Priyono, A., Ridwan, M., Alis, A.J., Rahmat: Generation of Fuzzy Rules with Subtractive Clustering. Journal Teknologi, 43(D), Dis., 143–153 (2005)
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Garvit, K., Goyal, H., Dwivedi, V.K., Mehta, V.K., Mehta, R. (2012). Designing of Quad Copter for Surveillance and Hydrological Data Collection: Maximizing Target Acquisition. In: Deep, K., Nagar, A., Pant, M., Bansal, J. (eds) Proceedings of the International Conference on Soft Computing for Problem Solving (SocProS 2011) December 20-22, 2011. Advances in Intelligent and Soft Computing, vol 131. Springer, New Delhi. https://doi.org/10.1007/978-81-322-0491-6_40
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DOI: https://doi.org/10.1007/978-81-322-0491-6_40
Publisher Name: Springer, New Delhi
Print ISBN: 978-81-322-0490-9
Online ISBN: 978-81-322-0491-6
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