Implementation of the CA-CFAR algorithm for pulsed-Doppler radar on a GPU architecture
The Cell-Averaging Constant False-Alarm Rate (CA-CFAR) algorithm was implemented and optimized in software on the NVIDIA Tesla C1060 GPU architecture for application in pulsed-Doppler radar signal processors. A systematic approach was followed to gradually explore opportunities for parallel executio...
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creator | Venter, C. J. Grobler, H. AlMalki, K. A. |
description | The Cell-Averaging Constant False-Alarm Rate (CA-CFAR) algorithm was implemented and optimized in software on the NVIDIA Tesla C1060 GPU architecture for application in pulsed-Doppler radar signal processors. A systematic approach was followed to gradually explore opportunities for parallel execution and optimization by implementing the algorithm first in MATLAB (CPU), followed by native C (CPU) and finally NVIDIA CUDA (GPU) environments. Three techniques for implementing the CA-CFAR in software were identified and implemented, namely a naïıve technique, sliding window technique and a new variant which employs the Summed-Area Table (SAT) algorithm. The naïıve technique performed best on the GPU architecture. The SAT technique shows potential, especially for cases where very large CFAR windows are required. However, the results do not justify using the GPU architecture instead of the CPU architecture for this application when data transfer to and from the GPU is taken into consideration. |
doi_str_mv | 10.1109/AEECT.2011.6132514 |
format | Conference Proceeding |
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However, the results do not justify using the GPU architecture instead of the CPU architecture for this application when data transfer to and from the GPU is taken into consideration.</description><identifier>ISBN: 1457710838</identifier><identifier>ISBN: 9781457710834</identifier><identifier>EISBN: 145771082X</identifier><identifier>EISBN: 9781457710841</identifier><identifier>EISBN: 9781457710827</identifier><identifier>EISBN: 1457710846</identifier><identifier>DOI: 10.1109/AEECT.2011.6132514</identifier><language>eng</language><publisher>IEEE</publisher><subject>Benchmark testing ; CFAR ; Computer architecture ; Doppler ; Doppler effect ; GPU ; Graphics processing unit ; Microprocessors ; Optimization ; radar ; signal processing ; Throughput</subject><ispartof>2011 IEEE Jordan Conference on Applied Electrical Engineering and Computing Technologies (AEECT), 2011, p.1-6</ispartof><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/6132514$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,776,780,785,786,2052,27902,54895</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/6132514$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Venter, C. J.</creatorcontrib><creatorcontrib>Grobler, H.</creatorcontrib><creatorcontrib>AlMalki, K. A.</creatorcontrib><title>Implementation of the CA-CFAR algorithm for pulsed-Doppler radar on a GPU architecture</title><title>2011 IEEE Jordan Conference on Applied Electrical Engineering and Computing Technologies (AEECT)</title><addtitle>AEECT</addtitle><description>The Cell-Averaging Constant False-Alarm Rate (CA-CFAR) algorithm was implemented and optimized in software on the NVIDIA Tesla C1060 GPU architecture for application in pulsed-Doppler radar signal processors. A systematic approach was followed to gradually explore opportunities for parallel execution and optimization by implementing the algorithm first in MATLAB (CPU), followed by native C (CPU) and finally NVIDIA CUDA (GPU) environments. Three techniques for implementing the CA-CFAR in software were identified and implemented, namely a naïıve technique, sliding window technique and a new variant which employs the Summed-Area Table (SAT) algorithm. The naïıve technique performed best on the GPU architecture. The SAT technique shows potential, especially for cases where very large CFAR windows are required. However, the results do not justify using the GPU architecture instead of the CPU architecture for this application when data transfer to and from the GPU is taken into consideration.</description><subject>Benchmark testing</subject><subject>CFAR</subject><subject>Computer architecture</subject><subject>Doppler</subject><subject>Doppler effect</subject><subject>GPU</subject><subject>Graphics processing unit</subject><subject>Microprocessors</subject><subject>Optimization</subject><subject>radar</subject><subject>signal processing</subject><subject>Throughput</subject><isbn>1457710838</isbn><isbn>9781457710834</isbn><isbn>145771082X</isbn><isbn>9781457710841</isbn><isbn>9781457710827</isbn><isbn>1457710846</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2011</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNpFkE9LxDAUxCMiqOt-Ab3kC7TmJemfHEvtrgsLilTxtry2L7bSbkuaPfjtLbjgXIaB-c1hGLsHEQII85gVRV6GUgCEMSgZgb5gt6CjJAGRys_L_6DSa7ae52-xKJZJGosb9rEbpp4GOnr03Xjko-W-JZ5nQb7J3jj2X6PrfDtwOzo-nfqZmuBpnBbGcYcNOr5AyLev7xxd3Xaean9ydMeuLC7l9dlXrNwUZf4c7F-2uzzbB50RPqikhbQhrZQWiSZSkUAVy0gLSBqb2rhqtExqXKLByhhTxxFUaNIKsKEqUiv28DfbEdFhct2A7udwvkH9AgbeUKY</recordid><startdate>201112</startdate><enddate>201112</enddate><creator>Venter, C. J.</creator><creator>Grobler, H.</creator><creator>AlMalki, K. A.</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>201112</creationdate><title>Implementation of the CA-CFAR algorithm for pulsed-Doppler radar on a GPU architecture</title><author>Venter, C. J. ; Grobler, H. ; AlMalki, K. 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A.</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP All) 1998-Present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Venter, C. J.</au><au>Grobler, H.</au><au>AlMalki, K. A.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Implementation of the CA-CFAR algorithm for pulsed-Doppler radar on a GPU architecture</atitle><btitle>2011 IEEE Jordan Conference on Applied Electrical Engineering and Computing Technologies (AEECT)</btitle><stitle>AEECT</stitle><date>2011-12</date><risdate>2011</risdate><spage>1</spage><epage>6</epage><pages>1-6</pages><isbn>1457710838</isbn><isbn>9781457710834</isbn><eisbn>145771082X</eisbn><eisbn>9781457710841</eisbn><eisbn>9781457710827</eisbn><eisbn>1457710846</eisbn><abstract>The Cell-Averaging Constant False-Alarm Rate (CA-CFAR) algorithm was implemented and optimized in software on the NVIDIA Tesla C1060 GPU architecture for application in pulsed-Doppler radar signal processors. A systematic approach was followed to gradually explore opportunities for parallel execution and optimization by implementing the algorithm first in MATLAB (CPU), followed by native C (CPU) and finally NVIDIA CUDA (GPU) environments. Three techniques for implementing the CA-CFAR in software were identified and implemented, namely a naïıve technique, sliding window technique and a new variant which employs the Summed-Area Table (SAT) algorithm. The naïıve technique performed best on the GPU architecture. The SAT technique shows potential, especially for cases where very large CFAR windows are required. However, the results do not justify using the GPU architecture instead of the CPU architecture for this application when data transfer to and from the GPU is taken into consideration.</abstract><pub>IEEE</pub><doi>10.1109/AEECT.2011.6132514</doi><tpages>6</tpages></addata></record> |
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subjects | Benchmark testing CFAR Computer architecture Doppler Doppler effect GPU Graphics processing unit Microprocessors Optimization radar signal processing Throughput |
title | Implementation of the CA-CFAR algorithm for pulsed-Doppler radar on a GPU architecture |
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