Optimization of unmanned aerial vehicles detection in conditions of signal-to-noise ratio variation

Aeronautical and Space-Rocket Engineering

Control and testing of flying vehicles and their systems


Аuthors

Abdulov R. N.1*, Asadov H. G.2**

1. Research Institute of the Ministry of Defense Industry of Azerbaijan Republic, 25, Rahib Mammadov str., Baku, AZ1123, Republic of Azerbaijan
2. Scientific and Research Institute of Aerospace Informatics, 159, Azadlig ave., Baku, AZ1106, Azerbaijan

*e-mail: Rauf.abdulov@mail.ru
**e-mail: asadzade@rambler.ru

Abstract

The problem of illegal unmanned aerial vehicles of various types detection and identification consists in their low flight heights, small sizes and high maneuverability. The presented article analyses the interrelated optimal selection of detection probability figures in the UAV-Radar system, corresponding to the minimum value of the signal-to-noise ratio at the output of the radar receiving part, i.e. the worst conditions of the UAV detection. The authors suggest a new setting of the problem, associated with several pulses detection at the radar input while the signal-to-noise ratio changing dynamically. The article considers the situation when the detection probability grows with time, and the integral of the sum of detection probability and false alarm probability is equal to a certain constant. In conditions of dynamically changing signal-to-noise ratio with account for the accepted condition of constancy of the integral of the sum while preserving the mutually inverse by nature character changing of detection probability and the alarm probability the problem of optimal interrelation above said probabilities values calculation the is being set. The optimization criterion was formulated in the form of the integral of the well-known expression, determining the interrelation between the signal-to-noise ratio minimum probabilities and false alarm. The gist of the formulated optimization problem consists in finding such probability dependence of false alarm from the detection probability in the series of operations of radar detection with growing detection probability, at which the minimum of the integrated value of minimum signal to noise ratios is reached, ensuring detection of point objects at each radaroperation. Based on the performed analysis the authors obtained the functional relationship of the false alarm probability from the detection probability for scenario, when a pinpoint target in the course of radar detection with growing detection probability is being detected at minimum achievable figure of integrated signal-to-noise ratio at the radar receiver input.

Keywords:

detection probability, radar-UAV system, false alarm probability, target functional, signal-to-noise ratio, optimization criterion

References

  1. Cao H., Brener N.E., Iyengar S.S. 3D large grid route planner for the autonomous underwater vehicles. International Journal of Intelligent Computing and Cybernetics, 2009, vol. 2, no. 3, pp. 455-476.

  2. Bell M.G.H. Hyperstar: a multi-path Astar algorithm for risk averse vehicle navigation. Transportation Research, part B: Methodological, 2009, vol. 43, no. 1, pp. 97-107. DOI: 10.1016/j.trb.2008.05.010

  3. Filippis L., Guglieri G., Quagliotti F. Path Planning Strategies for UAVS in 3D Environments. Journal of Intelligent Robotic Systems, 2012, vol. 65, no. 1-4, pp. 247-264. DOI: 10.1007/s10846-011-9568-2

  4. Jun M., DAndrea R. Path planning for unmanned aerial vehicles in uncertain and adversarial environments. Cooperative Control: Models, Applications and Algorithms. Springer, New York, NY, USA. 2003, vol. 1, pp. 95-110.

  5. Kirsanov A.P. Vestnik Moskovskogo aviatsionnogo instituta, 2017, vol. 24, no. 4, pp. 129-136.

  6. Grumondz V.T., Polishchuk M.A. Vestnik Moskovskogo aviatsionnogo instituta, 2014, vol. 21, no. 4, pp. 7-12.

  7. Shi W., Arabadjis G., Bishop B., Hill P., Plasse R., Yoder J. Detecting, Tracking and identifying Airborne Threats with Netted Sensor Fence. Sensor Fusion – Foundation and Applications: Edited Volume. Edited by Ciza Thomas. InTech, 2011. Chapter 8. DOI: 10.5772/17666

  8. Kratky M., Fuxa L. Mini UAVs Detection by Radar. International Conference on Military Technologies (ICMT), 2015. DOI: 10.1109/MILTECHS.2015.7153647

  9. Poullin D. UAV Detection and Localization Using Passive DVB-T Radar MFN and SFN. Science and Technology Organization, 2016, 10 p. DOI: 10.14339/STO-MP-SET-231-18-PDF

  10. Kuschel H. Approaching 80 years of passive radar. IEEE International Conference on Radar, Adelaide, Australia, 2013, pp. 213-217. DOI: 10.1109/RADAR.2013.6651987

  11. Colone F., Falcone P., Bongioanni C., Lombardo P. WiFi-based passive bistatic radar: data processing schemes and experimental results. IEEE Transactions on Aerospace and Electronic Systems (TAES), 2012, vol. 48, no. 2, pp. 1061-1079. DOI: 10.1109/TAES.2012.6178049

  12. Moses A., Rutherford M.J., Valavanis K.P. Radar – based detection and identification for miniature air vehicles. IEEE International Conference on Control Applications (CCA), CO, USA, 2011, pp. 933-940. DOI: 10.1109/CCA.2011.6044363

  13. Chen V., Miceli W., Himed B. Micro-doppler analysis in ISAR – review and perspectives. Radar Conference – Surveillance for a Safer World, Bordeaux, France, 2009, 1-6 p.

  14. Zaugg E.C., Hudson D.L., Long D.G. The BYU mu SAR: A Small, Student-Built SAR for UAV Operation. IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2006. DOI:10.1109/IGARSS.2006.110

  15. Abdulov R.N., Abdullaev N.A., Asadov Kh.G. Naukoemkie tekhnologii v kosmicheskikh issledovaniyakh Zemli, 2017, vol. 9, no. 4, pp. 14-21.

  16. Watson A., Ramirez C.V., Salud E. Predicting Visibility of Aircraft. PLoS One, 2009, vol. 4, no. 5, pp. 1-16. DOI: 10.1371/journal.pone.0005594

  17. Zhan W., Wang W., Chen N., Wang Ch. Efficient UAV Path Planning with Multiconstraints in a 3D Large Battlefield Environment. Mathematical Problems in Engineering, 2014, 11 p. DOI: 10.1155/2014/597092

  18. Skolnik M.I. Introduction to Radar systems. McCraw-Hill Publishing Company Limited New Delhi. 2001, 772 p.

  19. Aubersheim W.J. A closed-form approximation of Robertson's detection characteristics. Proceedings of the IEEE, 1981, vol. 69, no. 7, p. 839. DOI: 10.1109/PROC.1981.12082

  20. KTSO.RU – Kompleks tekhnicheskikh sredstv okhrany. Normativnaya dokumentatsiya, http://www.ktso.ru/normdoc9/r78-36-030-2013/r78-36-030-2013

mai.ru — informational site of MAI

Copyright © 1994-2024 by MAI