Advertisement

Chaos Optimization Applied to a Beamforming Algorithm for Source Location

  • Karla I. Fernandez-Ramirez
  • Arturo Baltazar
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11289)

Abstract

In this work, the delay and sum (DAS) beamforming algorithm commonly used in several areas of engineering and robotics is modified and implemented for the identification and localization of acoustic sources. Its classical approach uses a systematic scanning of all points in a given space domain to localize a disturbance source. DAS is efficient when the searching area is small, but it becomes time consuming when the area increases, or when its topology is unknown. Here, an algorithm that uses beamforming information and a chaotic search scheme for optimal target localization is proposed. The algorithm is performed in two stages: first, the entire work area is mapped using chaotic sequences to determine a vector with the locations with a high probability of finding a source. The second stage initiates a search for a global optimum using chaotic walk trajectories. The proposed algorithm is tested with known analytical functions and then implemented using a time domain simulation of acoustic field and an array of sensors. The algorithm performance for the synthetic signals was compared with the traditional systematic scan. The results showed a reduction in searching time of 90% with similar localization accuracy as the typical beamforming.

Keywords

Optimization Chaos Scanning 

Notes

Acknowledgements

The authors thank CONACYT for providing financial support through the project CB-286907.

References

  1. 1.
    Gertner, I., Zeevi, Y.Y.: Scanning strategies for target detection. In: Proceeding SPIE, Data Structure and Target Classification, Orlando, Florida, vol. 1470 (1991)Google Scholar
  2. 2.
    Benesty, J., Chen, J., Huang, Y.: Microphone Array Signal Processing, 1st edn. Springer, Berlin (2008)Google Scholar
  3. 3.
    Van Trees, H.L.: Optimum Array Processing Part IV of Detection Estimation, and Modulation Theory, 1st edn. Wiley, Canadá (2002)Google Scholar
  4. 4.
    Benkoski, S., Monticino, M., Weisinger, J.: A survey of the search theory literature. Naval Res. Logist. 38(4), 469–494 (1991)CrossRefGoogle Scholar
  5. 5.
    Liu, B., Wang, L., Jin, Y.H., Tang, F., Huang, D.X.: Improved particle swarm optimization combined with chaos. Chaos Solitons Fractals 25, 1261–1271 (2005)CrossRefGoogle Scholar
  6. 6.
    Alatas, B.: Chaotic bee colony algorithms for global numerical optimization. Expert Syst. Appl. 37, 5682–5687 (2010)CrossRefGoogle Scholar
  7. 7.
    Yang, D., Liu, Z., Zhou, J.: Chaos optimization algorithms based on chaotic maps with different probability distribution and search speed for global optimization. Commun. Nonlinear Sci. Numer. Simul. 19(4), 1229–1246 (2014)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Baltazar, A., Fernandez-Ramirez, K.I., Aranda-Sanchez, J.I.: A study of chaotic searching for their application in an ultrasonic scanner. Eng. Appl. Artif. Intell. 74, 271–279 (2018)CrossRefGoogle Scholar
  9. 9.
    Baker, G., Gollud, J.: Chaotic Dynamics: An Introduction, 2nd edn. Cambridge University Press, New York (1996)CrossRefGoogle Scholar
  10. 10.
    K-Wave. http://www.k-wave.org/. Accessed 21 May 2018

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Robotics and Advanced Manufacturing ProgramCentro de Investigación y de Estudios Avanzados del Instituto Politécnico Nacional–Unidad SaltilloRamos ArizpeMexico

Personalised recommendations