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Performance Analysis of Optimization Algorithms Using Chirp Signal

  • K. AnurajEmail author
  • S. S. Poorna
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 98)

Abstract

In order to evaluate the material charateristics and defects, different input signals are allowed to pass through the material. These signals are able to capture the hidden information regarding the material while traversing througnh it. These material signatures can be obtained by analyzing the reflected signals. This enables us to study the material properties and defects non-invasively. The different input signals can be modelled as Chirp signal, Gaussian echo, combination of echoes, etc. In this paper, analysis is done using chirp as the input signal. The parameter estimation is done using Maximum Likelihood and different optimization techniques are adopted for minimizing the error. Eventhough the results obtained for all optimization algorithms are comparable with the actual parameters, Levenberg-Marquardt algorithm gave the best fit, with minimum average absolute relative error.

Keywords

MLE Chirp Parameter estimation ARE AS LM SQP TRR 

References

  1. 1.
    Candy, J.V.: CHIRP-Like Signals: Estimation: Detection and Processing A Sequential Model-Based Approach. No. LLNL-TR-690337-REV-1. Lawrence Livermore National Lab (LLNL), Livermore, CA (United States) (2016)Google Scholar
  2. 2.
    Lahiri, A., Kundu, D., Mitra, A.: On parameter estimation of two dimensional chirp signals. Submitted for publication (2011)Google Scholar
  3. 3.
    Djuric, P.M., Kay, S.M.: Parameter estimation of chirp signals. IEEE Trans. Acoust. Speech Signal Process. 38(12), 2118–2126 (1990)CrossRefGoogle Scholar
  4. 4.
    Lu, Y., Demirli, R., Cardoso, G., Saniie, J.: A successive parameter estimation algorithm for chirplet signal decomposition. IEEE Trans. Ultrason. Ferroelectr. Freq. Control 53(11), 2121–2131 (2006)CrossRefGoogle Scholar
  5. 5.
    Anuraj, K., Poorna, S.S., Saikumar, C.: Ultrasonic signal modelling and parameter estimation: a comparative study using optimization algorithms. In: International Conference on Soft Computing Systems, pp. 99–107. Springer, Singapore (2018)Google Scholar
  6. 6.
    Liu, X., Yu, H.: Time-domain joint parameter estimation of chirp signal based on SVR. Math. Probl. Eng. (2013)Google Scholar
  7. 7.
    Kundu, D., Nandi, S.: Parameter estimation of chirp signals in presence of stationary noise. Stat. Sin. 18, 187–201 (2008)MathSciNetzbMATHGoogle Scholar
  8. 8.
    Laddada, S., Lemlikchi, S., Djelouah, H., Si-Chaib, M.O.: Ultrasonic parameter estimation using the maximum likelihood estimation. In: 2015 4th International Conference on Electrical Engineering (ICEE), pp. 1–4. IEEE (2015)Google Scholar
  9. 9.
    Demirli, R., Saniie, J.: Model-based estimation of ultrasonic echoes. Part I: analysis and algorithms. IEEE Trans. Ultrason. Ferroelectr. Freq. Control 48(3), 787–802 (2001)CrossRefGoogle Scholar
  10. 10.
    Aditya, N.R., Abhijeeth, K.S., Anuraj, K., Poorna, S.S.: Error analysis of optimization algorithms in ultrasonic parameter estimation. In: IEEE ICCIC, 13 December to 15 December, at Thiyagarajar College of Engineering, Madurai, Tamil Nadu (2018)Google Scholar
  11. 11.
    Sreekumar, V., Anuraj, K., Poorna, S.S., Aditya, N.R., Jeyasree, S., Abhijeeth, K.S., Ranganath, L., Reddy, K.K.S.: MSE analysis of optimization algorithms using chirp signal. In: 4th IEEE International Conference on Recent Trends on Electronics, Information & Communication Technology, RTEICT (2019)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Department of Electronics and Communication EngineeringAmrita Vishwa VidyapeethamAmritapuriIndia

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