Research Article
Optimum Parameter Estimation Under Additive Cauchy-Gaussian Mixture Noise
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@INPROCEEDINGS{10.1007/978-3-030-77569-8_16, author={Yuan Chen and Dingfan Zhang and Longting Huang}, title={Optimum Parameter Estimation Under Additive Cauchy-Gaussian Mixture Noise}, proceedings={Quality, Reliability, Security and Robustness in Heterogeneous Systems. 16th EAI International Conference, QShine 2020, Virtual Event, November 29--30, 2020, Proceedings}, proceedings_a={QSHINE}, year={2021}, month={6}, keywords={Cauchy distribution Gaussian distribution Additive mixture noise Maximum likelihood estimation Voigt function M-estimation Pseudo-Voigt function}, doi={10.1007/978-3-030-77569-8_16} }- Yuan Chen
Dingfan Zhang
Longting Huang
Year: 2021
Optimum Parameter Estimation Under Additive Cauchy-Gaussian Mixture Noise
QSHINE
Springer
DOI: 10.1007/978-3-030-77569-8_16
Abstract
In this paper, a mixture process is proposed for modelling the summation of Cauchy and Gaussian random variables. The probability density function (PDF) of the mixture can be derived as the Voigt profile. To further study the noise, the estimation of the constant model is taken as an illustration. Here the scenarios of both known and unknown density parameters are considered. The maximum likelihood estimator (MLE) with Voigt function is first employed to devised the optimal estimator. Then anM-estimator with pseudo-Voigt function is developed to improve the computational complexity of MLE. Simulation results indicate the superior of both proposals, which can attain the Cramér-Rao lower bound.
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