Automotive Radar Sensing with Sparse Linear Arrays Using One-Bit Hankel Matrix Completion
The design of sparse linear arrays has proven instrumental in the implementation of cost-effective and efficient automotive radar systems for high-resolution imaging. This paper investigates the impact of coarse quantization on measurements obtained from such arrays. To recover azimuth angles from q...
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| creator | Eamaz, Arian Yeganegi, Farhang Hu, Yunqiao Sun, Shunqiao Soltanalian, Mojtaba |
| description | The design of sparse linear arrays has proven instrumental in the
implementation of cost-effective and efficient automotive radar systems for
high-resolution imaging. This paper investigates the impact of coarse
quantization on measurements obtained from such arrays. To recover azimuth
angles from quantized measurements, we leverage the low-rank properties of the
constructed Hankel matrix. In particular, by addressing the one-bit Hankel
matrix completion problem through a developed singular value thresholding
algorithm, our proposed approach accurately estimates the azimuth angles of
interest. We provide comprehensive insights into recovery performance and the
required number of one-bit samples. The effectiveness of our proposed scheme is
underscored by numerical results, demonstrating successful reconstruction using
only one-bit data. |
| doi_str_mv | 10.48550/arxiv.2312.05423 |
| format | Article |
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implementation of cost-effective and efficient automotive radar systems for
high-resolution imaging. This paper investigates the impact of coarse
quantization on measurements obtained from such arrays. To recover azimuth
angles from quantized measurements, we leverage the low-rank properties of the
constructed Hankel matrix. In particular, by addressing the one-bit Hankel
matrix completion problem through a developed singular value thresholding
algorithm, our proposed approach accurately estimates the azimuth angles of
interest. We provide comprehensive insights into recovery performance and the
required number of one-bit samples. The effectiveness of our proposed scheme is
underscored by numerical results, demonstrating successful reconstruction using
only one-bit data.</description><identifier>DOI: 10.48550/arxiv.2312.05423</identifier><language>eng</language><creationdate>2023-12</creationdate><rights>http://creativecommons.org/licenses/by/4.0</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>228,230,780,885</link.rule.ids><linktorsrc>$$Uhttps://arxiv.org/abs/2312.05423$$EView_record_in_Cornell_University$$FView_record_in_$$GCornell_University$$Hfree_for_read</linktorsrc><backlink>$$Uhttps://doi.org/10.48550/arXiv.2312.05423$$DView paper in arXiv$$Hfree_for_read</backlink></links><search><creatorcontrib>Eamaz, Arian</creatorcontrib><creatorcontrib>Yeganegi, Farhang</creatorcontrib><creatorcontrib>Hu, Yunqiao</creatorcontrib><creatorcontrib>Sun, Shunqiao</creatorcontrib><creatorcontrib>Soltanalian, Mojtaba</creatorcontrib><title>Automotive Radar Sensing with Sparse Linear Arrays Using One-Bit Hankel Matrix Completion</title><description>The design of sparse linear arrays has proven instrumental in the
implementation of cost-effective and efficient automotive radar systems for
high-resolution imaging. This paper investigates the impact of coarse
quantization on measurements obtained from such arrays. To recover azimuth
angles from quantized measurements, we leverage the low-rank properties of the
constructed Hankel matrix. In particular, by addressing the one-bit Hankel
matrix completion problem through a developed singular value thresholding
algorithm, our proposed approach accurately estimates the azimuth angles of
interest. We provide comprehensive insights into recovery performance and the
required number of one-bit samples. The effectiveness of our proposed scheme is
underscored by numerical results, demonstrating successful reconstruction using
only one-bit data.</description><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>GOX</sourceid><recordid>eNotj8FOhDAYhHvxYFYfwJN9AZC2FMoRibqbYDZx14Mn8rf-aCMUUuq6-_aL6GSSOUwymY-QG5bEqZIyuQN_tIeYC8bjRKZcXJK38jsM_RDsAekLvIOnO3STdR_0x4ZPuhvBT0hr63CuSu_hNNHXpd86jO5toGtwX9jRZwjeHmk19GOHwQ7uily00E14_Z8rsn982FfrqN4-baqyjiDLRWR0gQZVxlnBZqc658ANzmeZzA0ypguTSiU1Ss1bzFtRtKjSWSqHRGdiRW7_Zhe2ZvS2B39qfhmbhVGcARH9TEM</recordid><startdate>20231208</startdate><enddate>20231208</enddate><creator>Eamaz, Arian</creator><creator>Yeganegi, Farhang</creator><creator>Hu, Yunqiao</creator><creator>Sun, Shunqiao</creator><creator>Soltanalian, Mojtaba</creator><scope>GOX</scope></search><sort><creationdate>20231208</creationdate><title>Automotive Radar Sensing with Sparse Linear Arrays Using One-Bit Hankel Matrix Completion</title><author>Eamaz, Arian ; Yeganegi, Farhang ; Hu, Yunqiao ; Sun, Shunqiao ; Soltanalian, Mojtaba</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a673-cb9ece8621911914b72a2ce550157ce11b9c4585be5b2fe7f39fe8444487a0b63</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><toplevel>online_resources</toplevel><creatorcontrib>Eamaz, Arian</creatorcontrib><creatorcontrib>Yeganegi, Farhang</creatorcontrib><creatorcontrib>Hu, Yunqiao</creatorcontrib><creatorcontrib>Sun, Shunqiao</creatorcontrib><creatorcontrib>Soltanalian, Mojtaba</creatorcontrib><collection>arXiv.org</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Eamaz, Arian</au><au>Yeganegi, Farhang</au><au>Hu, Yunqiao</au><au>Sun, Shunqiao</au><au>Soltanalian, Mojtaba</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Automotive Radar Sensing with Sparse Linear Arrays Using One-Bit Hankel Matrix Completion</atitle><date>2023-12-08</date><risdate>2023</risdate><abstract>The design of sparse linear arrays has proven instrumental in the
implementation of cost-effective and efficient automotive radar systems for
high-resolution imaging. This paper investigates the impact of coarse
quantization on measurements obtained from such arrays. To recover azimuth
angles from quantized measurements, we leverage the low-rank properties of the
constructed Hankel matrix. In particular, by addressing the one-bit Hankel
matrix completion problem through a developed singular value thresholding
algorithm, our proposed approach accurately estimates the azimuth angles of
interest. We provide comprehensive insights into recovery performance and the
required number of one-bit samples. The effectiveness of our proposed scheme is
underscored by numerical results, demonstrating successful reconstruction using
only one-bit data.</abstract><doi>10.48550/arxiv.2312.05423</doi><oa>free_for_read</oa></addata></record> |
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| title | Automotive Radar Sensing with Sparse Linear Arrays Using One-Bit Hankel Matrix Completion |
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