Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/32051
Title: Line spectral estimation with unlimited sensing
Authors: Wang, H
Fang, J
Li, H
Leus, G
Zhu, R
Gan, L
Keywords: unlimited sensing;line spectral estimation;modulo samples
Issue Date: 23-Jul-2025
Publisher: Elsevier
Citation: Wang, H. et al. (2026) 'Line spectral estimation with unlimited sensing', Signal Processing, 238, 110205, pp. 1 - 15. doi: 10.1016/j.sigpro.2025.110205.
Abstract: In the paper, we consider the line spectral estimation problem in an unlimited sensing framework (USF), where a modulo analog-to-digital converter (ADC) is employed to fold the input signal back into a bounded interval before quantization. Such an operation is mathematically equivalent to taking the modulo of the input signal with respect to the interval. To overcome the noise sensitivity of higher-order difference-based methods, we explore the properties of the first-order difference of modulo samples, and develop two line spectral estimation algorithms based on the first-order difference, which are robust against noise. Specifically, we show that, with a high probability, the first-order difference of the original samples is equivalent to that of the modulo samples. By utilizing this property, line spectral estimation is solved via a robust sparse signal recovery approach. The second algorithms is built on our finding that, with a sufficiently high sampling rate, the first-order difference of the original samples can be decomposed as a sum of the first-order difference of the modulo samples and a sequence whose elements are confined to three possible values. This decomposition enables us to formulate the line spectral estimation problem as a mixed integer linear program that can be efficiently solved. Simulation results show that both proposed methods are robust against noise and achieve a significant performance improvement over the higher-order difference-based method. methods.
Description: Data availability: Data will be made available on request.
URI: https://bura.brunel.ac.uk/handle/2438/32051
DOI: https://doi.org/10.1016/j.sigpro.2025.110205
ISSN: 0165-1684
Other Identifiers: ORCiD: Hongwei Wang https://orcid.org/0000-0002-3385-7284
ORCiD: Ruixiang Zhu https://orcid.org/0009-0006-7298-1401
ORCiD: Lu Gan https://orcid.org/0000-0003-1056-7660
Article number: 110205
Appears in Collections:Dept of Electronic and Electrical Engineering Embargoed Research Papers

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