Chirp Signal Model

2020 ◽  
pp. 179-216
Author(s):  
Swagata Nandi ◽  
Debasis Kundu
Keyword(s):  
2021 ◽  
Author(s):  
Subhra Sankar Dhar

<p>The parameters in the well-known chirp signal model controls the frequency fluctuations of the signals, and consequently, the estimation of the parameters has received considerable attention in the literature of statistical signal processing. In the same spirit with a broader view, this article investigates the quantile estimator of parameters involved in the chirp signal model, which enables us to provide basic features of the entire distribution of the signals. In the course of this study, we establish the limiting behaviour of the associated stochastic process, which we call quantile process. As the applications of this result, we obtain the limiting distributions of various quantile based measures of descriptive statistics, which give us summarized features of the fluctuations of the signals in various senses. Finally, along with extensive simulation study, the practicability of the proposed methodology is shown on a few benchmark real datasets closely related with various chirp signal models.<br></p>


2014 ◽  
Vol 2014 ◽  
pp. 1-14
Author(s):  
Jiawen Bian ◽  
Jing Xing ◽  
Zhihui Liu ◽  
Lihua Fu ◽  
Hongwei Li

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 fromOp(N-1)of the initial estimator toOp(N-3/2)for one-lag differential signal condition and fromOp(N-2)of the initial estimator toOp(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 orderN. 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.


2021 ◽  
Author(s):  
Subhra Sankar Dhar

<p>The parameters in the well-known chirp signal model controls the frequency fluctuations of the signals, and consequently, the estimation of the parameters has received considerable attention in the literature of statistical signal processing. In the same spirit with a broader view, this article investigates the quantile estimator of parameters involved in the chirp signal model, which enables us to provide basic features of the entire distribution of the signals. In the course of this study, we establish the limiting behaviour of the associated stochastic process, which we call quantile process. As the applications of this result, we obtain the limiting distributions of various quantile based measures of descriptive statistics, which give us summarized features of the fluctuations of the signals in various senses. Finally, along with extensive simulation study, the practicability of the proposed methodology is shown on a few benchmark real datasets closely related with various chirp signal models.<br></p>


2019 ◽  
Vol 67 (16) ◽  
pp. 4291-4301 ◽  
Author(s):  
Subhra Sankar Dhar ◽  
Debasis Kundu ◽  
Ujjwal Das
Keyword(s):  

Author(s):  
Andreas I. Koutrouvelis ◽  
Richard C. Hendriks ◽  
Richard Heusdens ◽  
Jesper Jensen

1988 ◽  
Vol 24 (15) ◽  
pp. 973 ◽  
Author(s):  
A. Ouslimani ◽  
G. Vernet ◽  
J.C. Henaux ◽  
P. Crozat ◽  
R. Adde

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