Abstract
Harmonic retrieval (HR) has a wide range of applications in the scenes where signals are modelled as a summation of sinusoids. Past works have developed a number of approaches to recover the original signals. Most of them rely on classical singular value decomposition, which are vulnerable to unexpected outliers. In this paper, to overcome this deficiency, we propose a new random-access HR model and develop robust algorithms combining \(L_1\)-Tucker decomposition methods of Hankel tensor and novel frequency recovery techniques to solve such HR problem. Simulations are designed to compare our proposed methods with some existing tensor-based algorithms for HR. The numerical results demonstrate the outlier-insensitivity of our methods.
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Communicated by Yimin Wei.
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This work was supported by the National Natural Science Foundation of China (Grant no. 12171271).
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Luan, Z., Ming, Z., Wu, Y. et al. Hankel tensor-based model and \(L_1\)-Tucker decomposition-based frequency recovery method for harmonic retrieval problem. Comp. Appl. Math. 42, 14 (2023). https://doi.org/10.1007/s40314-022-02151-3
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DOI: https://doi.org/10.1007/s40314-022-02151-3