Efficient algorithm for estimating the parameters of a chirp signal

Lahiri, Ananya; Kundu, Debasis; Mitra, Amit

doi:10.1016/j.jmva.2012.01.021

Cited by 12 publications

(13 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…where " L →" means converging in distribution and N 3 (0, 2 2 0 Σ) denotes the 3-variate normal distribution with mean vector zero and dispersion matrix 2 2 0 Σ. It can be seen [9] that the asymptotic variance of 2 is 90 4 / 5 4 which is the same as the CRLB [20,30].…”

Section: Existing Resultsmentioning

confidence: 97%

“…Once the LSE of ( 1 , 2 , ), say, (̂1,̂2,̂), is obtained, the LSE of can be easily obtained aŝ(̂1,̂2,̂), respectively; see [29]. The joint asymptotic distribution of the parameters can be inferred similarly as in [9,29] as follows:…”

Section: Existing Resultsmentioning

confidence: 99%

“…For a long time, the maximum likelihood estimation (MLE) has been the statistical optimal solution to the parameter estimation of LFM signal [7]. The LSE is also statistical efficient and the corresponding asymptotic variance attains the CRLB in condition of stationary noise [8,9]. The LSE is known to be identical with MLE at i.i.d.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

Bian

Xing

Liu

et al. 2014

Journal of Applied Mathematics

View full text Add to dashboard Cite

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.

show abstract

Section: Existing Resultsmentioning

confidence: 97%

Section: Existing Resultsmentioning

confidence: 99%

See 1 more Smart Citation

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

Bian

Xing

Liu

et al. 2014

Journal of Applied Mathematics

View full text Add to dashboard Cite

show abstract

“…Two different algorithms employed in the present paper for chirp rate estimation, suggested in References [23,24], are explained in this section.…”

Section: Different Chirp Rate Estimation Algorithmsmentioning

confidence: 99%

Exploiting Chirp Rate Estimation Methods to Improve Image Formation Quality of Synthetic Aperture Radar

Rabiee

Azad

Parhizgar

2021

International Journal of Aerospace Engineering

View full text Add to dashboard Cite

A common assumption in SAR image formation and processing algorithms is that the chirp rates of the transmitted and received radar signals are exactly the same. Dechirp processing is also done based on this common assumption. In real scenarios, the chirp rate of the received signal is different from that of the transmitted signal due to several reasons. In case the difference between the chirp rates of the transmitted and received signals is obvious, the demodulation and compression of the received pulse are not carried out precisely and defocusing the targets and the output images of the SAR processor results. In the present paper, a new technique is proposed to improve the image formation quality of SAR by exploiting chirp rate estimation methods. Based on the proposed technique, the chirp rate of the received signal is estimated, and then, dechirp is carried out by using a time-reversed complex conjugate filter constructed based on the estimated chirp rate. In this stage, the existing chirp rate estimation algorithms can be used. The quality of the output image is assessed using PSLR as a quantitative criterion and the average number of point target extension pixels along the azimuth direction. Simulation results indicated that the smaller the average number of point target extension pixels along with azimuth and the higher the PSLR average is, the better the output image quality would be. Therefore, output images obtained from the proposed method by exploiting chirp rate estimation algorithms would have a better quality with a higher PSLR average (14.1 and 13.6) and also the lower average number of point target extension pixels along the azimuth directions (2.1 and 4.9) than the common method with PSLR average (8.3) and an average number of point target extension pixels along the azimuth direction (7.1).

show abstract

“…, T , is frequently used in different field of sciences, for example, sonar, radar, communications systems, as well as in oceanography and geology. One may see Abatzoglou (1986), Kumaresan and Verma (1987), Djuric and Kay(1990), Gini et al (2000), Lin and Djuric (2000), Lahiri et al (2012Lahiri et al ( , 2014 and the references cited therein for details. Recently various types of parameter estimation techniques and their various properties have been studied for the signal (1), for example see Kumaresan and Verma (1987), Djuric and Kay (1990), Gini et al (2000), Nandi and Kundu (2004), Kundu Nandi and Kundu (2004) first provided the asymptotic properties of least square estimates (LSE) of the parameters involved in one dimensional chirp signal with i. i. d. error structure.…”

Section: Introductionmentioning

confidence: 99%

Single-step and multiple-step forecasting in one-dimensional single chirp signal using MCMC-based Bayesian analysis

Mazumder

2016

Communications in Statistics - Simulation and Computation

View full text Add to dashboard Cite

Chirp signals are frequently used in different areas of science and engineering. MCMC based Bayesian inference is done here for purpose of one step and multiple step prediction in case of one dimensional single chirp signal with i. i. d. error structure as well as dependent error structure with exponentially decaying covariances. We use Gibbs sampling technique and random walk MCMC to update the parameters. We perform total five simulation studies for illustration purpose. We also do some real data analysis to show how the method is working in practice.

show abstract

Efficient algorithm for estimating the parameters of a chirp signal

Cited by 12 publications

References 10 publications

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

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

Exploiting Chirp Rate Estimation Methods to Improve Image Formation Quality of Synthetic Aperture Radar

Single-step and multiple-step forecasting in one-dimensional single chirp signal using MCMC-based Bayesian analysis

Contact Info

Product

Resources

About