Fast Reweighted Smoothed l-Norm Near-Field Source Localization Based on Fourth-Order Statistics

Meidong Kuang; Jian Xie; Ling Wang; Yuexian Wang; Yanyun Gong

doi:10.1109/LCOMM.2021.3125984

Fast Reweighted Smoothed l-Norm Near-Field Source Localization Based on Fourth-Order Statistics

Meidong Kuang, Jian Xie, Ling Wang, Yuexian Wang, Yanyun Gong

School of Electronics and Information

Northwestern Polytechnical University Xian

Research output: Contribution to journal › Article › peer-review

9 Scopus citations

Abstract

The $l_{1}$ -norm-based sparse signal reconstruction has relatively satisfactory performance for near-field parameter estimation. However, its computational complexity is still much higher than that of most other near-field parameter estimation algorithms. In this letter, a reweighted smoothed $l_{0}$ -norm near-field parameter estimation algorithm is proposed based on the fourth-order cumulant statistics (FOC). The direction of arrival (DOA) and range parameters are solved respectively in the reweighted smoothed $l_{0}$ -norm sparse reconstruction method by a steepest ascent method. According to the numerical simulations, the proposed algorithm can achieve low-complexity and fast sparse parameter estimation. Finally the superiority of the proposed algorithm is proved through the comparison of simulation and calculation.

Original language	English
Pages (from-to)	74-78
Number of pages	5
Journal	IEEE Communications Letters
Volume	26
Issue number	1
DOIs	https://doi.org/10.1109/LCOMM.2021.3125984
State	Published - 1 Jan 2022

Keywords

and sparse parameter estimation
FOC
low-complexity
l₀-norm
Near-filed localization

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10.1109/LCOMM.2021.3125984

Cite this

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title = "Fast Reweighted Smoothed l-Norm Near-Field Source Localization Based on Fourth-Order Statistics",

abstract = "The $l_{1}$ -norm-based sparse signal reconstruction has relatively satisfactory performance for near-field parameter estimation. However, its computational complexity is still much higher than that of most other near-field parameter estimation algorithms. In this letter, a reweighted smoothed $l_{0}$ -norm near-field parameter estimation algorithm is proposed based on the fourth-order cumulant statistics (FOC). The direction of arrival (DOA) and range parameters are solved respectively in the reweighted smoothed $l_{0}$ -norm sparse reconstruction method by a steepest ascent method. According to the numerical simulations, the proposed algorithm can achieve low-complexity and fast sparse parameter estimation. Finally the superiority of the proposed algorithm is proved through the comparison of simulation and calculation.",

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AU - Kuang, Meidong

AU - Xie, Jian

AU - Wang, Ling

AU - Wang, Yuexian

AU - Gong, Yanyun

PY - 2022/1/1

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N2 - The $l_{1}$ -norm-based sparse signal reconstruction has relatively satisfactory performance for near-field parameter estimation. However, its computational complexity is still much higher than that of most other near-field parameter estimation algorithms. In this letter, a reweighted smoothed $l_{0}$ -norm near-field parameter estimation algorithm is proposed based on the fourth-order cumulant statistics (FOC). The direction of arrival (DOA) and range parameters are solved respectively in the reweighted smoothed $l_{0}$ -norm sparse reconstruction method by a steepest ascent method. According to the numerical simulations, the proposed algorithm can achieve low-complexity and fast sparse parameter estimation. Finally the superiority of the proposed algorithm is proved through the comparison of simulation and calculation.

AB - The $l_{1}$ -norm-based sparse signal reconstruction has relatively satisfactory performance for near-field parameter estimation. However, its computational complexity is still much higher than that of most other near-field parameter estimation algorithms. In this letter, a reweighted smoothed $l_{0}$ -norm near-field parameter estimation algorithm is proposed based on the fourth-order cumulant statistics (FOC). The direction of arrival (DOA) and range parameters are solved respectively in the reweighted smoothed $l_{0}$ -norm sparse reconstruction method by a steepest ascent method. According to the numerical simulations, the proposed algorithm can achieve low-complexity and fast sparse parameter estimation. Finally the superiority of the proposed algorithm is proved through the comparison of simulation and calculation.

KW - and sparse parameter estimation

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KW - low-complexity

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Fast Reweighted Smoothed l-Norm Near-Field Source Localization Based on Fourth-Order Statistics

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Keywords

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