Abstract
The symmetric \(\alpha \)-stable (S\(\alpha \)S) noise is commonly encountered in a variety of applications such as wireless communications and image processing. In this work, combining the linear prediction property and \(\ell _p\)-norm minimization, a robust frequency estimator is devised for a complex sinusoid in the presence of the S\(\alpha \)S noise. The proposed algorithm, based on the \(\ell _p\)-norm of the preprocessed linear prediction errors, can be regarded as the outlier-resistant version of the generalized weighted linear prediction frequency estimation approach. Computer simulations are conducted to contrast the performance of the proposed algorithm with three conventional frequency estimators. The results indicate that our method is robust to outliers and nearly optimal compared with Cramér–Rao lower bound.
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Acknowledgements
We would like to express our gratitude to the associate editor for handling our paper and the anonymous reviewers for their professional suggestions and criticism.
Funding
This work was supported by National Natural Science Foundation of China (Grant No. 61701021) and Fundamental Research Funds for the Central Universities (Grant No. FRF-TP-17-017A2, WUT: 2017 IVA 049).
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Yuan Chen proposed the algorithm and wrote the paper. Long-Ting Huang implemented the algorithm and performed the simulation. Xiao Long Yang and Hing Cheung So reviewed and revised the manuscript. All authors read and approved the manuscript.
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Chen, Y., Yang, X.L., Huang, LT. et al. Robust Frequency Estimation in Symmetric \(\alpha \)-Stable Noise. Circuits Syst Signal Process 37, 4637–4650 (2018). https://doi.org/10.1007/s00034-018-0762-3
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DOI: https://doi.org/10.1007/s00034-018-0762-3