Maximum likelihood parameter estimation of chirp signals based on MCMC

Yan Lin; Xiutan Wang; Yingning Peng; Jia Xu; Liping Zhang; Xianggen Xia

Maximum likelihood parameter estimation of chirp signals based on MCMC

Yan Lin, Xiutan Wang^*, Yingning Peng, Jia Xu, Liping Zhang, Xianggen Xia

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

1 Citation (Scopus)

Abstract

The imaging of moving targets by synthetic aperture radar (SAR) needs to accurately estimate the parameters of chirp return signals of moving targets. This paper presents a new method to obtain the maximum likelihood estimate of mono-component chirp parameters. The method merges the Markov chain Monte Carlo (MCMC) technique and mean likelihood estimation (MELE) with discrete chirpogram as the initial value selection method. Simulations and analyses showed that the parameter estimation performance of this method can attain the Cramer Rao bound (CRB) at low signal-to-noise ratio (SNR). The method is simple and can be implemented with modest amount of computations. The method jointly estimates the parameters with no error propagation effect.

Original language	English
Pages (from-to)	511-514
Number of pages	4
Journal	Qinghua Daxue Xuebao/Journal of Tsinghua University
Volume	44
Issue number	4
Publication status	Published - Apr 2004
Externally published	Yes

Keywords

Chirp signal
Markov chain Monte Carlo (MCMC)
Maximum likelihood (ML)
Signal detection and estimation

Cite this

@article{e89fc64180bd412da9fb3ffb153280f7,

title = "Maximum likelihood parameter estimation of chirp signals based on MCMC",

abstract = "The imaging of moving targets by synthetic aperture radar (SAR) needs to accurately estimate the parameters of chirp return signals of moving targets. This paper presents a new method to obtain the maximum likelihood estimate of mono-component chirp parameters. The method merges the Markov chain Monte Carlo (MCMC) technique and mean likelihood estimation (MELE) with discrete chirpogram as the initial value selection method. Simulations and analyses showed that the parameter estimation performance of this method can attain the Cramer Rao bound (CRB) at low signal-to-noise ratio (SNR). The method is simple and can be implemented with modest amount of computations. The method jointly estimates the parameters with no error propagation effect.",

keywords = "Chirp signal, Markov chain Monte Carlo (MCMC), Maximum likelihood (ML), Signal detection and estimation",

author = "Yan Lin and Xiutan Wang and Yingning Peng and Jia Xu and Liping Zhang and Xianggen Xia",

year = "2004",

month = apr,

language = "English",

volume = "44",

pages = "511--514",

journal = "Qinghua Daxue Xuebao/Journal of Tsinghua University",

issn = "1000-0054",

publisher = "Tsinghua University Press",

number = "4",

}

TY - JOUR

T1 - Maximum likelihood parameter estimation of chirp signals based on MCMC

AU - Lin, Yan

AU - Wang, Xiutan

AU - Peng, Yingning

AU - Xu, Jia

AU - Zhang, Liping

AU - Xia, Xianggen

PY - 2004/4

Y1 - 2004/4

N2 - The imaging of moving targets by synthetic aperture radar (SAR) needs to accurately estimate the parameters of chirp return signals of moving targets. This paper presents a new method to obtain the maximum likelihood estimate of mono-component chirp parameters. The method merges the Markov chain Monte Carlo (MCMC) technique and mean likelihood estimation (MELE) with discrete chirpogram as the initial value selection method. Simulations and analyses showed that the parameter estimation performance of this method can attain the Cramer Rao bound (CRB) at low signal-to-noise ratio (SNR). The method is simple and can be implemented with modest amount of computations. The method jointly estimates the parameters with no error propagation effect.

AB - The imaging of moving targets by synthetic aperture radar (SAR) needs to accurately estimate the parameters of chirp return signals of moving targets. This paper presents a new method to obtain the maximum likelihood estimate of mono-component chirp parameters. The method merges the Markov chain Monte Carlo (MCMC) technique and mean likelihood estimation (MELE) with discrete chirpogram as the initial value selection method. Simulations and analyses showed that the parameter estimation performance of this method can attain the Cramer Rao bound (CRB) at low signal-to-noise ratio (SNR). The method is simple and can be implemented with modest amount of computations. The method jointly estimates the parameters with no error propagation effect.

KW - Chirp signal

KW - Markov chain Monte Carlo (MCMC)

KW - Maximum likelihood (ML)

KW - Signal detection and estimation

UR - http://www.scopus.com/inward/record.url?scp=3142748135&partnerID=8YFLogxK

M3 - Article

AN - SCOPUS:3142748135

SN - 1000-0054

VL - 44

SP - 511

EP - 514

JO - Qinghua Daxue Xuebao/Journal of Tsinghua University

JF - Qinghua Daxue Xuebao/Journal of Tsinghua University

IS - 4

ER -

Maximum likelihood parameter estimation of chirp signals based on MCMC

Abstract

Keywords

Other files and links

Fingerprint

Cite this