Annals of the Institute of Statistical Mathematics

, Volume 56, Issue 3, pp 529–544 | Cite as

Asymptotic properties of the least squares estimators of the parameters of the chirp signals

  • Swagata Nandi
  • Debasis Kundu
Asymptotic Theory

Abstract

Chirp signals are quite common in different areas of science and engineering. In this paper we consider the asymptotic properties of the least squares estimators of the parameters of the chirp signals. We obtain the consistency property of the least squares estimators and also obtain the asymptotic distribution under the assumptions that the errors are independent and identically distributed. We also consider the generalized chirp signals and obtain the asymptotic properties of the least squares estimators of the unknown parameters. Finally we perform some simulations experiments to see how the asymptotic results behave for small sample and the performances are quite satisfactory.

Key words and phrases

Chirp signal least squares estimators asymptotic distribution consistent estimators 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Abatzoglou, T. (1986). Fast maximum likelihood joint estimation of frequency and frequency rate,IEEE Transactions on Aerospace and Electronic Systems,22, 708–714.Google Scholar
  2. Besson, O., Ghogho, M. and Swami, A. (1999). Parameter estimation for random amplitude chirp signals,IEEE Transactions on Signal Processing,47, 3208–3219.CrossRefGoogle Scholar
  3. Chung, K. L. (1974).A First Course in Probability, Academic Press, New York.Google Scholar
  4. Djuric, P. M. and Kay, S. M. (1990). Parameter estimation of chirp signals,IEEE Transactions on Acoustics, Speech and Signal Processing,38, 2118–2126.CrossRefGoogle Scholar
  5. Gini, F., Montanari, M. and Verrazzani, L. (2000). Estimation of chirp signals in compound Gaussian clutter: A cyclostationary approach,IEEE Transactions on Acoustics, Speech and Signal Processing,48(4), 1029–1039.Google Scholar
  6. Huang, D., Sando S. and Wen, L. (1999). Least squares estimation of polynomial phase signals via stochastic tree-search, ICASSP, Phoenix, Arizona, Paper No. 1355.Google Scholar
  7. Jennrich, R. I. (1969). Asymptotic properties of the nonlinear least squares estimators,Annals of Mathematical Statistics,40, 633–643.MathSciNetGoogle Scholar
  8. Kumaresan, R. and Verma, S. (1987). On estimating the parameters of chirp signals using rank reduction techniques,Proceedings of 21st Asilomar Conference, 555–558, Pacific Grove, California.Google Scholar
  9. Kundu, D. (1991). Asymptotic properties of the complex valued nonlinear regression model,Communications in Statistics: Theory and Methods,20, 3793–3803.MathSciNetGoogle Scholar
  10. Kundu, D. and Mitra, A. (1996). A note on the consistency of the undamped exponential signals model,Statistics,28, 25–33.MathSciNetGoogle Scholar
  11. Rao, C. R. (1973).Linear Statistical Inferences and Its Applications 2nd ed., Wiley and Sons, New York.Google Scholar
  12. Rihaczek, A. W. (1969).Principles of High Resolution Radar, McGraw Hill, New York.Google Scholar
  13. Saha, S. and Kay, S. (2002). Maximum likelihood parameter estimation of superimposed chirps using Monte Carlo importance sampling,IEEE Transactions on Signal Processing,50, 224–230.CrossRefGoogle Scholar
  14. Wu, C. F. J. (1981). Asymptotic theory of the nonlinear least squares estimation,Annals of Statistics,9, 501–513.MathSciNetGoogle Scholar

Copyright information

© The Institute of Statistical Mathematics 2004

Authors and Affiliations

  • Swagata Nandi
    • 1
  • Debasis Kundu
    • 2
  1. 1.Institut für Angewandte MathematikRuprecht-Karls-Universität HeidelbergHeidelbergGermany
  2. 2.Department of MathematicsIndian Institute of Technology KanpurKanpurIndia

Personalised recommendations