An Improved MSR-Based Data-Driven Detection Method Using Smoothing Pre-Processing
The mean spectral radius (MSR) which indicates data correlations is used as a test statistic in data-driven detection approaches based on random matrix theory (RMT). To further improve the detection performance of MSR-based detectors, a smoothing pre-processing method is proposed in this letter. By...
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| Veröffentlicht in: | IEEE signal processing letters 2021, Vol.28, p.444-448 |
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| creator | Yan, Yujia Wu, Guangxin Dong, Yang Bai, Yechao |
| description | The mean spectral radius (MSR) which indicates data correlations is used as a test statistic in data-driven detection approaches based on random matrix theory (RMT). To further improve the detection performance of MSR-based detectors, a smoothing pre-processing method is proposed in this letter. By performing a smoothing pre-processing step on the original sampled data, the divergence between the distributions of the MSR under different hypotheses will be increased, effectively improving the detection probabilities. The AR(1) model is used to illustrate the effect of the smoothing pre-processing coefficients on detection performance. The optimum smoothing coefficients under different AR(1) coefficients and the change of detection probability under certain choices of smoothing coefficients are analyzed. It is verified that the proposed smoothing pre-processing method can effectively improve detection performance by the simulation of low-frequency oscillation detection in colored noise under low signal-to-noise ratio and experiments on floating small target detection in sea clutter using IPIX datasets. |
| doi_str_mv | 10.1109/LSP.2021.3058008 |
| format | Article |
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To further improve the detection performance of MSR-based detectors, a smoothing pre-processing method is proposed in this letter. By performing a smoothing pre-processing step on the original sampled data, the divergence between the distributions of the MSR under different hypotheses will be increased, effectively improving the detection probabilities. The AR(1) model is used to illustrate the effect of the smoothing pre-processing coefficients on detection performance. The optimum smoothing coefficients under different AR(1) coefficients and the change of detection probability under certain choices of smoothing coefficients are analyzed. 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To further improve the detection performance of MSR-based detectors, a smoothing pre-processing method is proposed in this letter. By performing a smoothing pre-processing step on the original sampled data, the divergence between the distributions of the MSR under different hypotheses will be increased, effectively improving the detection probabilities. The AR(1) model is used to illustrate the effect of the smoothing pre-processing coefficients on detection performance. The optimum smoothing coefficients under different AR(1) coefficients and the change of detection probability under certain choices of smoothing coefficients are analyzed. It is verified that the proposed smoothing pre-processing method can effectively improve detection performance by the simulation of low-frequency oscillation detection in colored noise under low signal-to-noise ratio and experiments on floating small target detection in sea clutter using IPIX datasets.</description><subject>Autoregressive processes</subject><subject>Clutter</subject><subject>Coefficients</subject><subject>Correlation</subject><subject>Data models</subject><subject>Detectors</subject><subject>Eigenvalues and eigenfunctions</subject><subject>Matrix theory</subject><subject>mean spectral radius</subject><subject>Optimized production technology</subject><subject>random matrix theory</subject><subject>Signal to noise ratio</subject><subject>Smoothing</subject><subject>Smoothing methods</subject><subject>Statistical analysis</subject><subject>Target detection</subject><issn>1070-9908</issn><issn>1558-2361</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNo9kF1LwzAUhoMoOKf3gjcFrzNzkiZNLufmx6DD6dx1SNvUdbhmJt3Af2_KhlfnhfOcDx6EboGMAIh6yJeLESUURoxwSYg8QwPgXGLKBJzHTDKClSLyEl2FsCGRAMkH6H3cJrPtzruDrZL58gM_mhDT1HQGT31zsG0ytZ0tu8a1ydx2a1clq9C0X8ly61y37tPCW7zwrrShb1yji9p8B3tzqkO0en76nLzi_O1lNhnnuKQKOlzVGcsKyWnJlLQiE6KSdQWZqmUloBIFN5SnKaRZUacFGApSEpNmQIkxXFk2RPfHvfH5n70Nnd64vW_jSU05AcYAhIgUOVKldyF4W-udb7bG_2oguhenozjdi9MncXHk7jjSWGv_ccU4cKXYH1AtZ6g</recordid><startdate>2021</startdate><enddate>2021</enddate><creator>Yan, Yujia</creator><creator>Wu, Guangxin</creator><creator>Dong, Yang</creator><creator>Bai, Yechao</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. (IEEE)</general><scope>97E</scope><scope>RIA</scope><scope>RIE</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>7SP</scope><scope>8FD</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><orcidid>https://orcid.org/0000-0001-5244-674X</orcidid><orcidid>https://orcid.org/0000-0003-0759-8722</orcidid></search><sort><creationdate>2021</creationdate><title>An Improved MSR-Based Data-Driven Detection Method Using Smoothing Pre-Processing</title><author>Yan, Yujia ; Wu, Guangxin ; Dong, Yang ; Bai, Yechao</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c291t-df737b852c398e6766d8fd179f8d61d6b5a2544147bf4b1a21880a47120aa59e3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Autoregressive processes</topic><topic>Clutter</topic><topic>Coefficients</topic><topic>Correlation</topic><topic>Data models</topic><topic>Detectors</topic><topic>Eigenvalues and eigenfunctions</topic><topic>Matrix theory</topic><topic>mean spectral radius</topic><topic>Optimized production technology</topic><topic>random matrix theory</topic><topic>Signal to noise ratio</topic><topic>Smoothing</topic><topic>Smoothing methods</topic><topic>Statistical analysis</topic><topic>Target detection</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Yan, Yujia</creatorcontrib><creatorcontrib>Wu, Guangxin</creatorcontrib><creatorcontrib>Dong, Yang</creatorcontrib><creatorcontrib>Bai, Yechao</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Electronic Library (IEL)</collection><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><jtitle>IEEE signal processing letters</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Yan, Yujia</au><au>Wu, Guangxin</au><au>Dong, Yang</au><au>Bai, Yechao</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>An Improved MSR-Based Data-Driven Detection Method Using Smoothing Pre-Processing</atitle><jtitle>IEEE signal processing letters</jtitle><stitle>LSP</stitle><date>2021</date><risdate>2021</risdate><volume>28</volume><spage>444</spage><epage>448</epage><pages>444-448</pages><issn>1070-9908</issn><eissn>1558-2361</eissn><coden>ISPLEM</coden><abstract>The mean spectral radius (MSR) which indicates data correlations is used as a test statistic in data-driven detection approaches based on random matrix theory (RMT). To further improve the detection performance of MSR-based detectors, a smoothing pre-processing method is proposed in this letter. By performing a smoothing pre-processing step on the original sampled data, the divergence between the distributions of the MSR under different hypotheses will be increased, effectively improving the detection probabilities. The AR(1) model is used to illustrate the effect of the smoothing pre-processing coefficients on detection performance. The optimum smoothing coefficients under different AR(1) coefficients and the change of detection probability under certain choices of smoothing coefficients are analyzed. It is verified that the proposed smoothing pre-processing method can effectively improve detection performance by the simulation of low-frequency oscillation detection in colored noise under low signal-to-noise ratio and experiments on floating small target detection in sea clutter using IPIX datasets.</abstract><cop>New York</cop><pub>IEEE</pub><doi>10.1109/LSP.2021.3058008</doi><tpages>5</tpages><orcidid>https://orcid.org/0000-0001-5244-674X</orcidid><orcidid>https://orcid.org/0000-0003-0759-8722</orcidid></addata></record> |
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| source | IEEE Electronic Library (IEL) |
| subjects | Autoregressive processes Clutter Coefficients Correlation Data models Detectors Eigenvalues and eigenfunctions Matrix theory mean spectral radius Optimized production technology random matrix theory Signal to noise ratio Smoothing Smoothing methods Statistical analysis Target detection |
| title | An Improved MSR-Based Data-Driven Detection Method Using Smoothing Pre-Processing |
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