Work

DMRA: An Adaptive Line Spectrum Estimation Method Through Dynamical Multiresolution of Atoms

Year: 2025

Type: article

Abstract: We proposed a novel dense line spectrum super-resolution algorithm, the DMRA, that leverages dynamical multi-resolution of atoms technique to address the limitation of traditional compressed sensing methods when handling dense point-source signals. The algorithm utilizes a smoothtanhrelaxation function to replace thel0norm, promoting sparsity and jointly estimating the frequency atoms and complex gains. To reduce computational complexity and improve frequency estimation accuracy, a two-stage strategy was further introduced to dynamically adjust the number of the optimized degrees of freedom. Theoretical analysis were provided to validate the proposed method for multi-parameter estimations. DMRA presents excellent performance for the super-resolution of cluster-sparse signals, which is a typical scenario in different practical applications. It also outperforms the state-of-the-art methods in terms of frequency estimation accuracy and computational efficiency.

Source: IEEE Transactions on Signal Processing

DOI: 10.1109/tsp.2025.3559782

Authors: Mingguang Han, Yi Zeng, Xiaoguang Li, Tiejun Li

Institutions: Peking University, Hunan Normal University, Key Laboratory of Computing and Stochastic Mathematics (Ministry of Education)

Language: English

Topic: Sparse and Compressive Sensing Techniques

Subfield: Computational Mechanics

Field: Engineering

Domain: Physical Sciences

Open Access status: green

Funders: National Natural Science Foundation of China, National Key Research and Development Program of China

Awards: G2519258023, G3771929557, G677953881