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A Computationally Efficient Iterative Algorithm for Estimating the Parameter of Chirp Signal Model

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  • Jiawen Bian
  • Jing Xing
  • Zhihui Liu
  • Lihua Fu
  • Hongwei Li

Abstract

The parameter estimation of Chirp signal model in additive noises when all the noises are independently and identically distributed (i.i.d.) has been considered. A novel iterative algorithm is proposed to estimate the frequency rate of the considered model by constructing the iterative statistics with one‐lag and multilag differential signals. It is observed that the estimator for the iterative algorithm is consistent and works quite well in terms of biases and mean squared errors. Moreover, the convergence rate of the estimator is improved from Op(N−1) of the initial estimator to Op(N−3/2) for one‐lag differential signal condition and from Op(N−2) of the initial estimator to Op(N−5/2) for multilag differential signal condition, respectively, by only three iterations. The range of the lag is discussed and the optimal lag is obtained for the multilag differential signal condition when the lag is of order N. The estimator of frequency rate with optimal lag is very close to Cramer‐Rao lower bound (CRLB) as well as the asymptotic variance of least‐squares estimator (LSE) at moderate signal‐to‐noise ratio (SNR). Finally, simulation experiments are performed to verify the effectiveness of the algorithm.

Suggested Citation

  • Jiawen Bian & Jing Xing & Zhihui Liu & Lihua Fu & Hongwei Li, 2014. "A Computationally Efficient Iterative Algorithm for Estimating the Parameter of Chirp Signal Model," Journal of Applied Mathematics, John Wiley & Sons, vol. 2014(1).
    • Handle: RePEc:wly:jnljam:v:2014:y:2014:i:1:n:903426
    DOI: 10.1155/2014/903426
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    References listed on IDEAS

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    1. Swagata Nandi & Debasis Kundu, 2004. "Asymptotic properties of the least squares estimators of the parameters of the chirp signals," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 56(3), pages 529-544, September.
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