Matches in SemOpenAlex for { <https://semopenalex.org/work/W4205478103> ?p ?o ?g. }
- W4205478103 endingPage "156" @default.
- W4205478103 startingPage "156" @default.
- W4205478103 abstract "Radar target detection (RTD) is a fundamental but important process of the radar system, which is designed to differentiate and measure targets from a complex background. Deep learning methods have gained great attention currently and have turned out to be feasible solutions in radar signal processing. Compared with the conventional RTD methods, deep learning-based methods can extract features automatically and yield more accurate results. Applying deep learning to RTD is considered as a novel concept. In this paper, we review the applications of deep learning in the field of RTD and summarize the possible limitations. This work is timely due to the increasing number of research works published in recent years. We hope that this survey will provide guidelines for future studies and applications of deep learning in RTD and related areas of radar signal processing." @default.
- W4205478103 created "2022-01-25" @default.
- W4205478103 creator A5013719267 @default.
- W4205478103 creator A5044139442 @default.
- W4205478103 creator A5061201566 @default.
- W4205478103 creator A5062334788 @default.
- W4205478103 date "2022-01-04" @default.
- W4205478103 modified "2023-10-17" @default.
- W4205478103 title "Artificial Neural Networks and Deep Learning Techniques Applied to Radar Target Detection: A Review" @default.
- W4205478103 cites W1536680647 @default.
- W4205478103 cites W1920235975 @default.
- W4205478103 cites W1994616650 @default.
- W4205478103 cites W2005646389 @default.
- W4205478103 cites W2006342232 @default.
- W4205478103 cites W2020105744 @default.
- W4205478103 cites W2025328969 @default.
- W4205478103 cites W2051812123 @default.
- W4205478103 cites W2063146632 @default.
- W4205478103 cites W2063907334 @default.
- W4205478103 cites W2064220837 @default.
- W4205478103 cites W2066549015 @default.
- W4205478103 cites W2070960269 @default.
- W4205478103 cites W2073780961 @default.
- W4205478103 cites W2086577481 @default.
- W4205478103 cites W2088779313 @default.
- W4205478103 cites W2094800231 @default.
- W4205478103 cites W2102605133 @default.
- W4205478103 cites W2102940528 @default.
- W4205478103 cites W2109255472 @default.
- W4205478103 cites W2112796928 @default.
- W4205478103 cites W2116531303 @default.
- W4205478103 cites W2123913800 @default.
- W4205478103 cites W2135117342 @default.
- W4205478103 cites W2140515654 @default.
- W4205478103 cites W2156019871 @default.
- W4205478103 cites W2161381512 @default.
- W4205478103 cites W2163922914 @default.
- W4205478103 cites W2242223225 @default.
- W4205478103 cites W2259806492 @default.
- W4205478103 cites W2292481059 @default.
- W4205478103 cites W2322513635 @default.
- W4205478103 cites W2507782627 @default.
- W4205478103 cites W2552002155 @default.
- W4205478103 cites W2556115712 @default.
- W4205478103 cites W2565547434 @default.
- W4205478103 cites W2618530766 @default.
- W4205478103 cites W2621220614 @default.
- W4205478103 cites W2625643843 @default.
- W4205478103 cites W2626654349 @default.
- W4205478103 cites W2632026579 @default.
- W4205478103 cites W2746870488 @default.
- W4205478103 cites W2770967835 @default.
- W4205478103 cites W2774016410 @default.
- W4205478103 cites W2789436454 @default.
- W4205478103 cites W2806165570 @default.
- W4205478103 cites W2897109612 @default.
- W4205478103 cites W2900741051 @default.
- W4205478103 cites W2902353552 @default.
- W4205478103 cites W2903903899 @default.
- W4205478103 cites W2911419173 @default.
- W4205478103 cites W2919115771 @default.
- W4205478103 cites W2921542497 @default.
- W4205478103 cites W2940726923 @default.
- W4205478103 cites W2963689780 @default.
- W4205478103 cites W2969276548 @default.
- W4205478103 cites W2973452878 @default.
- W4205478103 cites W2973595938 @default.
- W4205478103 cites W2974225875 @default.
- W4205478103 cites W2979508273 @default.
- W4205478103 cites W3096831136 @default.
- W4205478103 cites W3126873108 @default.
- W4205478103 cites W3163842339 @default.
- W4205478103 cites W3167147114 @default.
- W4205478103 cites W3205393884 @default.
- W4205478103 cites W3208912836 @default.
- W4205478103 cites W4239510810 @default.
- W4205478103 cites W639708223 @default.
- W4205478103 doi "https://doi.org/10.3390/electronics11010156" @default.
- W4205478103 hasPublicationYear "2022" @default.
- W4205478103 type Work @default.
- W4205478103 citedByCount "13" @default.
- W4205478103 countsByYear W42054781032022 @default.
- W4205478103 countsByYear W42054781032023 @default.
- W4205478103 crossrefType "journal-article" @default.
- W4205478103 hasAuthorship W4205478103A5013719267 @default.
- W4205478103 hasAuthorship W4205478103A5044139442 @default.
- W4205478103 hasAuthorship W4205478103A5061201566 @default.
- W4205478103 hasAuthorship W4205478103A5062334788 @default.
- W4205478103 hasBestOaLocation W42054781031 @default.
- W4205478103 hasConcept C104267543 @default.
- W4205478103 hasConcept C108583219 @default.
- W4205478103 hasConcept C111919701 @default.
- W4205478103 hasConcept C119857082 @default.
- W4205478103 hasConcept C154945302 @default.
- W4205478103 hasConcept C202444582 @default.
- W4205478103 hasConcept C2987759513 @default.
- W4205478103 hasConcept C33923547 @default.
- W4205478103 hasConcept C41008148 @default.