Power Spectral Density Estimation of Radiated Noise with Sparse Spectral Fitting

Guoqing Jiang; Chao Sun; Xionghou Liu; Guangyu Jiang; Wenjun Duan

doi:10.1109/IEEECONF38699.2020.9389169

Power Spectral Density Estimation of Radiated Noise with Sparse Spectral Fitting

Guoqing Jiang, Chao Sun, Xionghou Liu, Guangyu Jiang, Wenjun Duan

School of Marine Science and Technology

Northwestern Polytechnical University Xian

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

When measuring the radiated noise from a noncooperative target by a small-aperture vertical array, the location of the noise source is usually unknown, so most measurement methods are ineffective. To solve this problem, we improve the sparse spectral fitting (SpSF) method so that it can be used to estimate the power spectral density (PSD) of radiated noise even though the source location is unknown. First, the experiment area is discretized and the sparse representation model of the received data is established. Then the PSD expression of the radiated noise is established by the covariance matrix in frequency-domain. Finally, the PSD and location of the radiated noise can be estimated simultaneously by the SpSF method. Simulation examples are given to demonstrate the performance of PSD estimation by SpSF.

Original language	English
Title of host publication	2020 Global Oceans 2020
Subtitle of host publication	Singapore - U.S. Gulf Coast
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781728154466
DOIs	https://doi.org/10.1109/IEEECONF38699.2020.9389169
State	Published - 5 Oct 2020
Event	2020 Global Oceans: Singapore - U.S. Gulf Coast, OCEANS 2020 - Biloxi, United States Duration: 5 Oct 2020 → 30 Oct 2020

Publication series

Name	2020 Global Oceans 2020: Singapore - U.S. Gulf Coast

Conference

Conference	2020 Global Oceans: Singapore - U.S. Gulf Coast, OCEANS 2020
Country/Territory	United States
City	Biloxi
Period	5/10/20 → 30/10/20

Keywords

non-cooperative target
power spectral density estimation
Radiated noise measurement
sparse spectral fitting (SpSF)

Access to Document

10.1109/IEEECONF38699.2020.9389169

Cite this

Jiang, G., Sun, C., Liu, X., Jiang, G., & Duan, W. (2020). Power Spectral Density Estimation of Radiated Noise with Sparse Spectral Fitting. In 2020 Global Oceans 2020: Singapore - U.S. Gulf Coast Article 9389169 (2020 Global Oceans 2020: Singapore - U.S. Gulf Coast). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IEEECONF38699.2020.9389169

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title = "Power Spectral Density Estimation of Radiated Noise with Sparse Spectral Fitting",

abstract = "When measuring the radiated noise from a noncooperative target by a small-aperture vertical array, the location of the noise source is usually unknown, so most measurement methods are ineffective. To solve this problem, we improve the sparse spectral fitting (SpSF) method so that it can be used to estimate the power spectral density (PSD) of radiated noise even though the source location is unknown. First, the experiment area is discretized and the sparse representation model of the received data is established. Then the PSD expression of the radiated noise is established by the covariance matrix in frequency-domain. Finally, the PSD and location of the radiated noise can be estimated simultaneously by the SpSF method. Simulation examples are given to demonstrate the performance of PSD estimation by SpSF.",

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author = "Guoqing Jiang and Chao Sun and Xionghou Liu and Guangyu Jiang and Wenjun Duan",

note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 2020 Global Oceans: Singapore - U.S. Gulf Coast, OCEANS 2020 ; Conference date: 05-10-2020 Through 30-10-2020",

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doi = "10.1109/IEEECONF38699.2020.9389169",

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Jiang, G, Sun, C , Liu, X, Jiang, G & Duan, W 2020, Power Spectral Density Estimation of Radiated Noise with Sparse Spectral Fitting. in 2020 Global Oceans 2020: Singapore - U.S. Gulf Coast., 9389169, 2020 Global Oceans 2020: Singapore - U.S. Gulf Coast, Institute of Electrical and Electronics Engineers Inc., 2020 Global Oceans: Singapore - U.S. Gulf Coast, OCEANS 2020, Biloxi, United States, 5/10/20. https://doi.org/10.1109/IEEECONF38699.2020.9389169

Power Spectral Density Estimation of Radiated Noise with Sparse Spectral Fitting. / Jiang, Guoqing; Sun, Chao ; Liu, Xionghou et al.
2020 Global Oceans 2020: Singapore - U.S. Gulf Coast. Institute of Electrical and Electronics Engineers Inc., 2020. 9389169 (2020 Global Oceans 2020: Singapore - U.S. Gulf Coast).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Power Spectral Density Estimation of Radiated Noise with Sparse Spectral Fitting

AU - Jiang, Guoqing

AU - Sun, Chao

AU - Liu, Xionghou

AU - Jiang, Guangyu

AU - Duan, Wenjun

PY - 2020/10/5

Y1 - 2020/10/5

N2 - When measuring the radiated noise from a noncooperative target by a small-aperture vertical array, the location of the noise source is usually unknown, so most measurement methods are ineffective. To solve this problem, we improve the sparse spectral fitting (SpSF) method so that it can be used to estimate the power spectral density (PSD) of radiated noise even though the source location is unknown. First, the experiment area is discretized and the sparse representation model of the received data is established. Then the PSD expression of the radiated noise is established by the covariance matrix in frequency-domain. Finally, the PSD and location of the radiated noise can be estimated simultaneously by the SpSF method. Simulation examples are given to demonstrate the performance of PSD estimation by SpSF.

AB - When measuring the radiated noise from a noncooperative target by a small-aperture vertical array, the location of the noise source is usually unknown, so most measurement methods are ineffective. To solve this problem, we improve the sparse spectral fitting (SpSF) method so that it can be used to estimate the power spectral density (PSD) of radiated noise even though the source location is unknown. First, the experiment area is discretized and the sparse representation model of the received data is established. Then the PSD expression of the radiated noise is established by the covariance matrix in frequency-domain. Finally, the PSD and location of the radiated noise can be estimated simultaneously by the SpSF method. Simulation examples are given to demonstrate the performance of PSD estimation by SpSF.

KW - non-cooperative target

KW - power spectral density estimation

KW - Radiated noise measurement

KW - sparse spectral fitting (SpSF)

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DO - 10.1109/IEEECONF38699.2020.9389169

M3 - 会议稿件

AN - SCOPUS:85104590055

T3 - 2020 Global Oceans 2020: Singapore - U.S. Gulf Coast

BT - 2020 Global Oceans 2020

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2020 Global Oceans: Singapore - U.S. Gulf Coast, OCEANS 2020

Y2 - 5 October 2020 through 30 October 2020

ER -

Power Spectral Density Estimation of Radiated Noise with Sparse Spectral Fitting

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