Research Article
Robust Frequency Estimation Under Additive Mixture Noise
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@INPROCEEDINGS{10.1007/978-3-030-77569-8_9, author={Yuan Chen and Dingfan Zhang and Longting Huang}, title={Robust Frequency Estimation Under Additive 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={Frequency estimation Additive Cauchy-Gaussian mixture noise Metropolis-Hastings algorithm Cram\^{e}r-Rao lower bound}, doi={10.1007/978-3-030-77569-8_9} }- Yuan Chen
Dingfan Zhang
Longting Huang
Year: 2021
Robust Frequency Estimation Under Additive Mixture Noise
QSHINE
Springer
DOI: 10.1007/978-3-030-77569-8_9
Abstract
In this paper, we address the frequency estimation problem of a single sinusoid embedded in the heavy-tailed noise, where the additive Cauchy-Gaussian mixture (ACG) model is considered. Here the ACG noise model is the sum of Gaussian and Cauchy variables. With the use of Metropolis-Hastings algorithm, an accurate frequency estimator is developed in the presence of ACG noise. Simulation results demonstrate that the mean square error performance of the proposed algorithm can attain the Cramér-Rao lower bound.
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