Cramer-Rao Lower Bound for Parameter Estimation of Multiexponential Signals

Jibia, Abdussamad Umar; Salami, Momoh Jimoh Eyiomika; Khalifa, Othman O.; Elfaki, Faiz Ahmed Mohamed

doi:10.1109/iwssip.2009.5367779

Cited by 5 publications

(4 citation statements)

References 16 publications

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“…6, to get the better and high resolution of the reconstructed image. Now we are driving the estimation of the noise present in the sample by using Cramer Rao lower bound [22]. And have the minimum covariance for our estimation noise model, we developed the likelihood function as, when it will aperture filter sampling, we neglect constant parameter, because it is fixed parameter and we keep it constant so that it does not affect the signal and the aperture coefficient , we are driving the estimation of the noise present in the sample by using the Cramer Rao lower bound and it is widely used for estimation of the parameters [22].…”

Section: Noise Modelingmentioning

confidence: 99%

Aperture Filter and Adaptive Filter Response for Reconstruction of Sea Surface Image Based on LIDAR Observation

Sheikh¹,

QunSheng²,

Wang³

2015

IJMO

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This paper discusses the reconstruction of seasurface photo imaging based on Light Detection and Ranging (LIDAR) scanning observation, the new framework consists of three phases. Firstly, we set the angles and range of the image to perform the scanning to get the pixel coordinates of seasurface image in x and y direction respectively. Secondly, we get the continuous signal and sampled by using aperture filter and combine the patches into the piecewise form to achieved the smoothly reconstruction. Finally, we applied the adaptive filter to increases the resolution of the image and to filter out the noise present in the image. The proposed method demonstrates the better reconstruction of sea-surface image. In addition we performed error simulation and the noise evaluation of the aperture filter, and it is noted that the error is significantly reduced compared with other method.

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Section: Noise Modelingmentioning

confidence: 99%

Aperture Filter and Adaptive Filter Response for Reconstruction of Sea Surface Image Based on LIDAR Observation

Sheikh¹,

QunSheng²,

Wang³

2015

IJMO

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“…시정수 추정기 의 설계시 Cramer-Rao (C-R) bound 는 설계된 추정기의 분산 (variance) 의 한계를 결정짓는 기준으로 성능 평가시 주요한 평가 대상으로 사용될 수 있다 [3]. 선행 연구에서는 주로 가우시안 (Gaussian) 잡음 하에서의 지수 감소함수의 인자 추정에 관한 연구들이 수행되었고 가우시안 잡음 하에 서의 C-R bound 에 관한 연구도 수행되었다 [1] [3]. 그러나, 형광 감소와 같이 광자 (photon) [6].…”

Section: 서 론 측정된 실험치로부터 지수적으로 감소하는 함수의 크기unclassified

On the Cramer-Rao Bound for Estimating Parameters of Exponentially Decaying Function under Poisson Noise

Seok¹,

Kim²

2013

The Transactions of The Korean Institute of Electrical Engineer

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-We computed Cramer-Rao bound for estimating amplitude and decay parameters of exponentially decaying function under Poisson noise. Since Cramer-Rao bound is the lowest variance bound for any unbiased estimator, the computed Cramer-Rao bound can be used for evaluating the performance of estimators under Poisson noise. In addition, we show that the performance of maximum-likelihood estimator is close to the Cramer-Rao bound by simulations.

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“…By varying the data length and comparing of the resulting estimates with the CRLB we can know which data length would produce the best estimator. Derivation of the CRLB was done as presented in [6] The reasons for the choice of this signal were given in [10]. Simulations using the proposed ARMA algorithm with conventional inverse filtering showed that the spectrum is good only for…”

Section: A Determination Of the Truncation Pointmentioning

confidence: 99%

“…Specifically, only two parameters are used and all others suppressed. Another improvement over the approach in [3] is the fact that whereas in [3] the truncation point of the deconvolved data was determined by trial and adjustment, in this paper Cramer Rao Lower Bound as derived and used in [6,7] is used to determine the good length of the deconvolved data. A number of simulations were carried out to test the efficacy of the proposed combination using different synthetic signals.…”

Section: Introductionmentioning

confidence: 99%

Parameter Estimation of Transient Multiexponential Signals Using SVD-ARMA and Multiparameter Deconvolution Techniques

Jibia¹,

Salami²

2012

IJCTE

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Much has been reported about the analysis of transient multiexponentials data. In a previous paper, for example, this analysis was done using autoregressive moving average model which was applied to the deconvolved data arising from the application of Gardner transform followed by optimal compensation deconvolution to the original signal. Optimal compensation deconvolution uses a single parameter noise-reduction parameter. In this paper, a deconvolution parameter incorporating multiple noise-reduction parameters is used instead. Simulations and experimental results show that the proposed combination, despite its limitations supersedes several existing methods.

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Cramer-Rao Lower Bound for Parameter Estimation of Multiexponential Signals

Cited by 5 publications

References 16 publications

Aperture Filter and Adaptive Filter Response for Reconstruction of Sea Surface Image Based on LIDAR Observation

Aperture Filter and Adaptive Filter Response for Reconstruction of Sea Surface Image Based on LIDAR Observation

On the Cramer-Rao Bound for Estimating Parameters of Exponentially Decaying Function under Poisson Noise

Parameter Estimation of Transient Multiexponential Signals Using SVD-ARMA and Multiparameter Deconvolution Techniques

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