Robust Source Number Estimation Based on Denoising Preprocessing

Ichige, Koichi; Hamada, Shohei; Kashiwagi, Katsuhisa; Arakawa, Nobuya; Saito, Ryo

doi:10.1109/sspd47486.2020.9272068

Cited by 2 publications

(3 citation statements)

References 9 publications

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“…The first step of these methods involves roughly distinguishing the signal and noise using the difference in the singular values. The second step involves using the singular value ratio to detect the source with small power [18].…”

Section: Proposed Methodsmentioning

confidence: 99%

“…We propose two methods for accurate and robust source-number estimation for array antennas and multiinput multi-output (MIMO) radar. We first apply denoising preprocessing by removing undesired frequency com-ponents by bandpass filtering [18], [19], then detect noise using the singular value ratio of the original signals and those of denoised signals. Denoising preprocessing affects only the noise components; therefore, the singular values corresponding to noise become smaller.…”

Section: Introductionmentioning

confidence: 99%

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Accurate Source-Number Estimation Using Denoising Preprocessing and Singular Value Decomposition

Hamada

Ichige

Kashiwagi

et al. 2022

IEICE Trans. Commun.

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This paper proposes two accurate source-number estimation methods for array antennas and multi-input multi-output radar. Direction of arrival (DOA) estimation is important in high-speed wireless communication and radar imaging. Most representative DOA estimation methods require the source-number information in advance and often fail to estimate DOAs in severe environments such as those having low signalto-noise ratio or large transmission-power difference. Received signals are often bandlimited or narrowband signals, so the proposed methods first involves denoising preprocessing by removing undesired components then comparing the original and denoised signal information. The performances of the proposed methods were evaluated through computer simulations * * * .

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Section: Proposed Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Accurate Source-Number Estimation Using Denoising Preprocessing and Singular Value Decomposition

Hamada

Ichige

Kashiwagi

et al. 2022

IEICE Trans. Commun.

Self Cite

View full text Add to dashboard Cite

show abstract

“…To avoid phase ambiguity, it is assumed that spacing and the number of sensors meets . The number of sources can be used directly as prior knowledge by using the method in [26,27]. For analysis convenience, the sources are FFSs, and the ( sources are NFSs.…”

Section: Signal Modelmentioning

confidence: 99%

Localization for Mixed Near-Field and Far-Field Sources under Impulsive Noise

Gao,

Zhang,

et al. 2024

J. of Syst. Eng. Electron.

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In order to solve the problem that the performance of traditional localization methods for mixed near-field sources (NFSs) and far-field sources (FFSs) degrades under impulsive noise, a robust and novel localization method is proposed. After eliminating the impacts of impulsive noise by the weighted outlier filter, the direction of arrivals (DOAs) of FFSs can be estimated by multiple signal classification (MUSIC) spectral peaks search. Based on the DOAs information of FFSs, the separation of mixed sources can be performed. Finally, the estimation of localizing parameters of NFSs can avoid two-dimension spectral peaks search by decomposing steering vectors. The Cramer-Rao bounds (CRB) for the unbiased estimations of DOA and range under impulsive noise have been drawn. Simulation experiments verify that the proposed method has advantages in probability of successful estimation (PSE) and root mean square error (RMSE) compared with existing localization methods. It can be concluded that the proposed method is effective and reliable in the environment with low generalized signal to noise ratio (GSNR), few snapshots, and strong impulse.

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Robust Source Number Estimation Based on Denoising Preprocessing

Cited by 2 publications

References 9 publications

Accurate Source-Number Estimation Using Denoising Preprocessing and Singular Value Decomposition

Accurate Source-Number Estimation Using Denoising Preprocessing and Singular Value Decomposition

Localization for Mixed Near-Field and Far-Field Sources under Impulsive Noise

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