Abstract
The performance of a narrowband active noise control (NANC) system can be significantly degraded due to the frequency mismatch (FM). In this paper, the statistical performance of a typical FxLMS-based NANC system in the presence of FM is analyzed in detail. Difference equations governing the system dynamics and closed-form steady-state mean-square error expressions are derived and discussed. The stochastic analysis results reveal that the FM introduces small troublesome sinusoids into the NANC system. The controller has to track these sinusoids to minimize the residual noise, which leads to a serious performance deterioration. As a by-product, the optimal step sizes that minimize the effect of a relatively small FM are also derived. The findings significantly enrich our understanding of the stochastic behavior of the FxLMS algorithm in the presence of FM and also provide some useful information for NANC system design. Extensive simulations are conducted to confirm the validity of the analytical findings.
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The authors declare that all data supporting the findings of this study are available within the article.
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Acknowledgements
This work was supported in part by the National Natural Science Foundations of China (Nos. 61971219, 61201364) and the Fundamental Research Funds for the Central Universities (NO. NJ2022016). Parts of this work were presented in the 2012 IEEE International Symposium on Intelligent Signal Processing and Communications Systems. The authors would like to thank the Associate Editor and anonymous reviewers for providing rich and instructive feedback and suggestions to improve the quality and presentation of the paper.
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JL contributed to conceptualization; JL contributed to methodology; JL contributed to software; JL involved in validation; JL involved in formal analysis; JL involved in data curation; JL involved in writing—original draft preparation; HC involved in writing—review and editing; JL and HC involved in funding acquisition; all authors have read and agreed to the published version of the manuscript.
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Appendices
Appendix A: Derivation of (18)
Through \(\textbf{D}_i\) defined by (13) in [17], one easily has
and from the third step of \(\Delta \textbf{G}_i(n)\) given by (32) in [17] without applying Taylor series expansions, we have
where
Then, substituting \(\textbf{P}_i(n)\) and \(\textbf{Q}_i(n)\), respectively, defined by (29) and (30) in [17], into (15) yields (18).
Appendix B: Some Terms Used in Deriving Difference Eqs. (23) and (24)
Here, \(\textrm{sgn}(\cdot )\) and \(\delta (\cdot )\) are signum and Dirac delta functions, respectively.
Appendix C: Derivations of the Steady-State Time-Averaged MSEs (30) and (31)
Signals \(\Delta g_{a_i}(n)\), \(\Delta g_{b_i}(n)\) given in (A1) and (A2) can be recursively calculated by
Cross multiplying (21) and (22) by (C11) and (C12), one can obtain the following recursive equations:
where
From (A1), (A2), and (C13)–(C16), considering the time averaging manipulations in the steady state, one readily yields
where
Solving simultaneous equations composed of the above four equations, one easily reaches
Now, using the relations obtained above after taking the time averaging of (23) and (24), we have (30) and (31).
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Liu, J., Chen, H. Insights into the Filtered-x LMS Algorithm in the Presence of Frequency Mismatch. Circuits Syst Signal Process 43, 2909–2936 (2024). https://doi.org/10.1007/s00034-023-02578-x
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DOI: https://doi.org/10.1007/s00034-023-02578-x