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Meta-heuristics for Improved RF Emitter Localization

  • Sondre A. Engebråten
  • Jonas Moen
  • Kyrre Glette
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10200)

Abstract

Locating Radio Frequency (RF) emitters can be done with a number of methods, but cheap and widely available sensors make the Power Difference of Arrival (PDOA) technique a prominent choice. Predicting the location of an unknown RF emitter can be seen as a continuous optimization problem, minimizing the error w.r.t. the sensor measurements gathered. Most instances of this problem feature multi-modality, making these challenging to solve. This paper presents an analysis of the performance of evolutionary computation and other meta-heuristic methods on this real-world problem. We applied the Nelder-Mead method, Genetic Algorithm, Covariance Matrix Adaptation Evolutionary Strategies, Particle Swarm Optimization and Differential Evolution. The use of meta-heuristics solved the minimization problem more efficiently and precisely, compared to brute force search, potentially allowing for a more widespread use of the PDOA method. To compare algorithms two different metrics were proposed: average distance miss and median distance miss, giving insight into the algorithms’ performance. Finally, the use of an adaptive mutation step proved important.

Keywords

Search heuristics Continuous optimization Multilateration 

Notes

Acknowledgments

We would like to thank Ingebjørg Kåsen and Eilif Solberg for their assistance with the statistical issues in this paper and Jørgen Nordmoen for enlightening discussions and excellent feedback.

References

  1. 1.
    Berle, F.: Mixed triangulation/trilateration technique for emitter location. IEE Proc. F Commun. Radar Signal Process 133(7), 638–641 (1986)Google Scholar
  2. 2.
    Eberhart, R., Kennedy, J.: A new optimizer using particle swarm theory. In: Proceedings of the Sixth International Symposium on Micro Machine and Human Science, MHS 1995, pp. 39–43. IEEE (1995)Google Scholar
  3. 3.
    Eiben, A.E., Smith, J.E.: Introduction to Evolutionary Computing. Springer Science & Business Media, Heidelberg (2003)Google Scholar
  4. 4.
    Engebråten, S.A.: RF Emitter geolocation using PDOA algorithms and UAVs. Master’s thesis, Norwegian University of Science and Technology (2015)Google Scholar
  5. 5.
    Fortin, F.-A., De Rainville, F.-M., Gardner, M.-A., Parizeau, M., Gagné, C.: DEAP: evolutionary algorithms made easy. J. Mach. Learn. Res. 13, 2171–2175 (2012)MathSciNetzbMATHGoogle Scholar
  6. 6.
    Goldberg, D.E., Holland, J.H.: Genetic algorithms and machine learning. Mach. Learn. 3(2), 95–99 (1988)CrossRefGoogle Scholar
  7. 7.
    Hansen, N., Müller, S.D., Koumoutsakos, P.: Reducing the time complexity of the derandomized evolution strategy with covariance matrix adaptation (CMA-ES). Evol. Comput. 11(1), 1–18 (2003)CrossRefGoogle Scholar
  8. 8.
    Hansen, N., Ostermeier, A.: Adapting arbitrary normal mutation distributions in evolution strategies: the covariance matrix adaptation. In: Proceedings of IEEE International Conference on Evolutionary Computation, pp. 312–317. IEEE (1996)Google Scholar
  9. 9.
    Huning, A., Rechenberg, I., Eigen, M.: Evolutionsstrategie. optimierung technischer systeme nach prinzipien der biologischen evolution (1976)Google Scholar
  10. 10.
    Jackson, B., Wang, S., Inkol, R.: Emitter geolocation estimation using power difference of arrival. Defence R&D Canada Technical report DRDC Ottawa TR, 40 (2011)Google Scholar
  11. 11.
    Levanon, N.: Radar Principles, 320 p. Wiley-Interscience, New York (1988)Google Scholar
  12. 12.
    Nelder, J.A., Mead, R.: A simplex method for function minimization. Comput. J. 7(4), 308–313 (1965)MathSciNetCrossRefzbMATHGoogle Scholar
  13. 13.
    Nordmoen, J.: Detecting a hidden radio frequency transmitter in noise based on amplitude using swarm intelligence. Master’s thesis, Norwegian University of Science and Technology, 6 (2014)Google Scholar
  14. 14.
    Saunders, S., Aragón-Zavala, A.: Antennas and Propagation for Wireless Communication Systems. John Wiley & Sons, Chichester (2007)Google Scholar
  15. 15.
    Staras, H., Honickman, S.N.: The accuracy of vehicle location by trilateration in a dense urban environment. IEEE Trans. Veh. Technol. 21(1), 38–43 (1972)CrossRefGoogle Scholar
  16. 16.
    Storn, R., Price, K.: Differential evolution-a simple and efficient heuristic for global optimization over continuous spaces. J. Global Optim. 11(4), 341–359 (1997)MathSciNetCrossRefzbMATHGoogle Scholar
  17. 17.
    Wang, Z., Blasch, E., Chen, G., Shen, D., Lin, X., Pham, K.: A low-cost, near-real-time two-UAS-based UWB emitter monitoring system. IEEE Aerosp. Electron. Syst. Mag. 30(11), 4–11 (2015)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Sondre A. Engebråten
    • 1
    • 2
  • Jonas Moen
    • 1
  • Kyrre Glette
    • 1
    • 2
  1. 1.Norwegian Defence Research EstablishmentKjellerNorway
  2. 2.University of OsloOsloNorway

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