RASPNet: A Benchmark Dataset for Radar Adaptive Signal Processing Applications Article Swipe
Shyam Venkatasubramanian
,
Bosung Kang
,
Ali Pezeshki
,
Muralidhar Rangaswamy
,
Vahid Tarokh
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2406.09638
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2406.09638
We present a large-scale dataset called RASPNet for radar adaptive signal processing (RASP) applications to support the development of data-driven models within the adaptive radar community. RASPNet exceeds 16 TB in size and comprises 100 realistic scenarios compiled over a variety of topographies and land types across the contiguous United States. For each scenario, RASPNet comprises 10,000 clutter realizations from an airborne radar setting, which can be used to benchmark radar and complex-valued learning algorithms. RASPNet intends to fill a prominent gap in the availability of a large-scale, realistic dataset that standardizes the evaluation of RASP techniques and complex-valued neural networks. We outline its construction, organization, and several applications, including a transfer learning example to demonstrate how RASPNet can be used for real-world adaptive radar scenarios.
Related Topics
Concepts
Benchmark (surveying)
Radar
Computer science
Radar signal processing
Signal processing
SIGNAL (programming language)
Space-time adaptive processing
Artificial intelligence
Real-time computing
Data mining
Telecommunications
Continuous-wave radar
Geography
Radar imaging
Cartography
Programming language
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2406.09638
- https://arxiv.org/pdf/2406.09638
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4399758759
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4399758759Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.48550/arxiv.2406.09638Digital Object Identifier
- Title
-
RASPNet: A Benchmark Dataset for Radar Adaptive Signal Processing ApplicationsWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-06-14Full publication date if available
- Authors
-
Shyam Venkatasubramanian, Bosung Kang, Ali Pezeshki, Muralidhar Rangaswamy, Vahid TarokhList of authors in order
- Landing page
-
https://arxiv.org/abs/2406.09638Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2406.09638Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2406.09638Direct OA link when available
- Concepts
-
Benchmark (surveying), Radar, Computer science, Radar signal processing, Signal processing, SIGNAL (programming language), Space-time adaptive processing, Artificial intelligence, Real-time computing, Data mining, Telecommunications, Continuous-wave radar, Geography, Radar imaging, Cartography, Programming languageTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4399758759 |
|---|---|
| doi | https://doi.org/10.48550/arxiv.2406.09638 |
| ids.doi | https://doi.org/10.48550/arxiv.2406.09638 |
| ids.openalex | https://openalex.org/W4399758759 |
| fwci | |
| type | preprint |
| title | RASPNet: A Benchmark Dataset for Radar Adaptive Signal Processing Applications |
| biblio.issue | |
| biblio.volume | |
| biblio.last_page | |
| biblio.first_page | |
| topics[0].id | https://openalex.org/T10891 |
| topics[0].field.id | https://openalex.org/fields/22 |
| topics[0].field.display_name | Engineering |
| topics[0].score | 0.7985000014305115 |
| topics[0].domain.id | https://openalex.org/domains/3 |
| topics[0].domain.display_name | Physical Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/2202 |
| topics[0].subfield.display_name | Aerospace Engineering |
| topics[0].display_name | Radar Systems and Signal Processing |
| topics[1].id | https://openalex.org/T10655 |
| topics[1].field.id | https://openalex.org/fields/22 |
| topics[1].field.display_name | Engineering |
| topics[1].score | 0.7422000169754028 |
| topics[1].domain.id | https://openalex.org/domains/3 |
| topics[1].domain.display_name | Physical Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/2202 |
| topics[1].subfield.display_name | Aerospace Engineering |
| topics[1].display_name | GNSS positioning and interference |
| topics[2].id | https://openalex.org/T10320 |
| topics[2].field.id | https://openalex.org/fields/17 |
| topics[2].field.display_name | Computer Science |
| topics[2].score | 0.741100013256073 |
| topics[2].domain.id | https://openalex.org/domains/3 |
| topics[2].domain.display_name | Physical Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/1702 |
| topics[2].subfield.display_name | Artificial Intelligence |
| topics[2].display_name | Neural Networks and Applications |
| is_xpac | False |
| apc_list | |
| apc_paid | |
| concepts[0].id | https://openalex.org/C185798385 |
| concepts[0].level | 2 |
| concepts[0].score | 0.7672025561332703 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q1161707 |
| concepts[0].display_name | Benchmark (surveying) |
| concepts[1].id | https://openalex.org/C554190296 |
| concepts[1].level | 2 |
| concepts[1].score | 0.6502033472061157 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q47528 |
| concepts[1].display_name | Radar |
| concepts[2].id | https://openalex.org/C41008148 |
| concepts[2].level | 0 |
| concepts[2].score | 0.6231542825698853 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[2].display_name | Computer science |
| concepts[3].id | https://openalex.org/C2987759513 |
| concepts[3].level | 4 |
| concepts[3].score | 0.559132993221283 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q47528 |
| concepts[3].display_name | Radar signal processing |
| concepts[4].id | https://openalex.org/C104267543 |
| concepts[4].level | 3 |
| concepts[4].score | 0.5024120807647705 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q208163 |
| concepts[4].display_name | Signal processing |
| concepts[5].id | https://openalex.org/C2779843651 |
| concepts[5].level | 2 |
| concepts[5].score | 0.4832330644130707 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q7390335 |
| concepts[5].display_name | SIGNAL (programming language) |
| concepts[6].id | https://openalex.org/C190203512 |
| concepts[6].level | 5 |
| concepts[6].score | 0.4507470428943634 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q7572182 |
| concepts[6].display_name | Space-time adaptive processing |
| concepts[7].id | https://openalex.org/C154945302 |
| concepts[7].level | 1 |
| concepts[7].score | 0.35040751099586487 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[7].display_name | Artificial intelligence |
| concepts[8].id | https://openalex.org/C79403827 |
| concepts[8].level | 1 |
| concepts[8].score | 0.3230617642402649 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q3988 |
| concepts[8].display_name | Real-time computing |
| concepts[9].id | https://openalex.org/C124101348 |
| concepts[9].level | 1 |
| concepts[9].score | 0.32188597321510315 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q172491 |
| concepts[9].display_name | Data mining |
| concepts[10].id | https://openalex.org/C76155785 |
| concepts[10].level | 1 |
| concepts[10].score | 0.19469988346099854 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q418 |
| concepts[10].display_name | Telecommunications |
| concepts[11].id | https://openalex.org/C59584813 |
| concepts[11].level | 4 |
| concepts[11].score | 0.18554720282554626 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q1029234 |
| concepts[11].display_name | Continuous-wave radar |
| concepts[12].id | https://openalex.org/C205649164 |
| concepts[12].level | 0 |
| concepts[12].score | 0.17664742469787598 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q1071 |
| concepts[12].display_name | Geography |
| concepts[13].id | https://openalex.org/C10929652 |
| concepts[13].level | 3 |
| concepts[13].score | 0.16367298364639282 |
| concepts[13].wikidata | https://www.wikidata.org/wiki/Q7279985 |
| concepts[13].display_name | Radar imaging |
| concepts[14].id | https://openalex.org/C58640448 |
| concepts[14].level | 1 |
| concepts[14].score | 0.10201805830001831 |
| concepts[14].wikidata | https://www.wikidata.org/wiki/Q42515 |
| concepts[14].display_name | Cartography |
| concepts[15].id | https://openalex.org/C199360897 |
| concepts[15].level | 1 |
| concepts[15].score | 0.0 |
| concepts[15].wikidata | https://www.wikidata.org/wiki/Q9143 |
| concepts[15].display_name | Programming language |
| keywords[0].id | https://openalex.org/keywords/benchmark |
| keywords[0].score | 0.7672025561332703 |
| keywords[0].display_name | Benchmark (surveying) |
| keywords[1].id | https://openalex.org/keywords/radar |
| keywords[1].score | 0.6502033472061157 |
| keywords[1].display_name | Radar |
| keywords[2].id | https://openalex.org/keywords/computer-science |
| keywords[2].score | 0.6231542825698853 |
| keywords[2].display_name | Computer science |
| keywords[3].id | https://openalex.org/keywords/radar-signal-processing |
| keywords[3].score | 0.559132993221283 |
| keywords[3].display_name | Radar signal processing |
| keywords[4].id | https://openalex.org/keywords/signal-processing |
| keywords[4].score | 0.5024120807647705 |
| keywords[4].display_name | Signal processing |
| keywords[5].id | https://openalex.org/keywords/signal |
| keywords[5].score | 0.4832330644130707 |
| keywords[5].display_name | SIGNAL (programming language) |
| keywords[6].id | https://openalex.org/keywords/space-time-adaptive-processing |
| keywords[6].score | 0.4507470428943634 |
| keywords[6].display_name | Space-time adaptive processing |
| keywords[7].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[7].score | 0.35040751099586487 |
| keywords[7].display_name | Artificial intelligence |
| keywords[8].id | https://openalex.org/keywords/real-time-computing |
| keywords[8].score | 0.3230617642402649 |
| keywords[8].display_name | Real-time computing |
| keywords[9].id | https://openalex.org/keywords/data-mining |
| keywords[9].score | 0.32188597321510315 |
| keywords[9].display_name | Data mining |
| keywords[10].id | https://openalex.org/keywords/telecommunications |
| keywords[10].score | 0.19469988346099854 |
| keywords[10].display_name | Telecommunications |
| keywords[11].id | https://openalex.org/keywords/continuous-wave-radar |
| keywords[11].score | 0.18554720282554626 |
| keywords[11].display_name | Continuous-wave radar |
| keywords[12].id | https://openalex.org/keywords/geography |
| keywords[12].score | 0.17664742469787598 |
| keywords[12].display_name | Geography |
| keywords[13].id | https://openalex.org/keywords/radar-imaging |
| keywords[13].score | 0.16367298364639282 |
| keywords[13].display_name | Radar imaging |
| keywords[14].id | https://openalex.org/keywords/cartography |
| keywords[14].score | 0.10201805830001831 |
| keywords[14].display_name | Cartography |
| language | en |
| locations[0].id | pmh:oai:arXiv.org:2406.09638 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S4306400194 |
| locations[0].source.issn | |
| locations[0].source.type | repository |
| locations[0].source.is_oa | True |
| locations[0].source.issn_l | |
| locations[0].source.is_core | False |
| locations[0].source.is_in_doaj | False |
| locations[0].source.display_name | arXiv (Cornell University) |
| locations[0].source.host_organization | https://openalex.org/I205783295 |
| locations[0].source.host_organization_name | Cornell University |
| locations[0].source.host_organization_lineage | https://openalex.org/I205783295 |
| locations[0].license | |
| locations[0].pdf_url | https://arxiv.org/pdf/2406.09638 |
| locations[0].version | submittedVersion |
| locations[0].raw_type | text |
| locations[0].license_id | |
| locations[0].is_accepted | False |
| locations[0].is_published | False |
| locations[0].raw_source_name | |
| locations[0].landing_page_url | http://arxiv.org/abs/2406.09638 |
| locations[1].id | doi:10.48550/arxiv.2406.09638 |
| locations[1].is_oa | True |
| locations[1].source.id | https://openalex.org/S4306400194 |
| locations[1].source.issn | |
| locations[1].source.type | repository |
| locations[1].source.is_oa | True |
| locations[1].source.issn_l | |
| locations[1].source.is_core | False |
| locations[1].source.is_in_doaj | False |
| locations[1].source.display_name | arXiv (Cornell University) |
| locations[1].source.host_organization | https://openalex.org/I205783295 |
| locations[1].source.host_organization_name | Cornell University |
| locations[1].source.host_organization_lineage | https://openalex.org/I205783295 |
| locations[1].license | cc-by |
| locations[1].pdf_url | |
| locations[1].version | |
| locations[1].raw_type | article |
| locations[1].license_id | https://openalex.org/licenses/cc-by |
| locations[1].is_accepted | False |
| locations[1].is_published | |
| locations[1].raw_source_name | |
| locations[1].landing_page_url | https://doi.org/10.48550/arxiv.2406.09638 |
| indexed_in | arxiv, datacite |
| authorships[0].author.id | https://openalex.org/A5058322190 |
| authorships[0].author.orcid | https://orcid.org/0000-0002-1400-9642 |
| authorships[0].author.display_name | Shyam Venkatasubramanian |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Venkatasubramanian, Shyam |
| authorships[0].is_corresponding | False |
| authorships[1].author.id | https://openalex.org/A5075951419 |
| authorships[1].author.orcid | https://orcid.org/0000-0001-9429-4537 |
| authorships[1].author.display_name | Bosung Kang |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Kang, Bosung |
| authorships[1].is_corresponding | False |
| authorships[2].author.id | https://openalex.org/A5028995727 |
| authorships[2].author.orcid | https://orcid.org/0000-0002-7142-5685 |
| authorships[2].author.display_name | Ali Pezeshki |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Pezeshki, Ali |
| authorships[2].is_corresponding | False |
| authorships[3].author.id | https://openalex.org/A5064635322 |
| authorships[3].author.orcid | https://orcid.org/0000-0003-4830-7637 |
| authorships[3].author.display_name | Muralidhar Rangaswamy |
| authorships[3].author_position | middle |
| authorships[3].raw_author_name | Rangaswamy, Muralidhar |
| authorships[3].is_corresponding | False |
| authorships[4].author.id | https://openalex.org/A5020766546 |
| authorships[4].author.orcid | https://orcid.org/0000-0003-2994-6302 |
| authorships[4].author.display_name | Vahid Tarokh |
| authorships[4].author_position | last |
| authorships[4].raw_author_name | Tarokh, Vahid |
| authorships[4].is_corresponding | False |
| has_content.pdf | False |
| has_content.grobid_xml | False |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://arxiv.org/pdf/2406.09638 |
| open_access.oa_status | green |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | RASPNet: A Benchmark Dataset for Radar Adaptive Signal Processing Applications |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T10891 |
| primary_topic.field.id | https://openalex.org/fields/22 |
| primary_topic.field.display_name | Engineering |
| primary_topic.score | 0.7985000014305115 |
| primary_topic.domain.id | https://openalex.org/domains/3 |
| primary_topic.domain.display_name | Physical Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/2202 |
| primary_topic.subfield.display_name | Aerospace Engineering |
| primary_topic.display_name | Radar Systems and Signal Processing |
| related_works | https://openalex.org/W2366373155, https://openalex.org/W2134802063, https://openalex.org/W2168601191, https://openalex.org/W2990360750, https://openalex.org/W2086165553, https://openalex.org/W2133719452, https://openalex.org/W2167654771, https://openalex.org/W4388228679, https://openalex.org/W2346071497, https://openalex.org/W77323478 |
| cited_by_count | 0 |
| locations_count | 2 |
| best_oa_location.id | pmh:oai:arXiv.org:2406.09638 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S4306400194 |
| best_oa_location.source.issn | |
| best_oa_location.source.type | repository |
| best_oa_location.source.is_oa | True |
| best_oa_location.source.issn_l | |
| best_oa_location.source.is_core | False |
| best_oa_location.source.is_in_doaj | False |
| best_oa_location.source.display_name | arXiv (Cornell University) |
| best_oa_location.source.host_organization | https://openalex.org/I205783295 |
| best_oa_location.source.host_organization_name | Cornell University |
| best_oa_location.source.host_organization_lineage | https://openalex.org/I205783295 |
| best_oa_location.license | |
| best_oa_location.pdf_url | https://arxiv.org/pdf/2406.09638 |
| best_oa_location.version | submittedVersion |
| best_oa_location.raw_type | text |
| best_oa_location.license_id | |
| best_oa_location.is_accepted | False |
| best_oa_location.is_published | False |
| best_oa_location.raw_source_name | |
| best_oa_location.landing_page_url | http://arxiv.org/abs/2406.09638 |
| primary_location.id | pmh:oai:arXiv.org:2406.09638 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S4306400194 |
| primary_location.source.issn | |
| primary_location.source.type | repository |
| primary_location.source.is_oa | True |
| primary_location.source.issn_l | |
| primary_location.source.is_core | False |
| primary_location.source.is_in_doaj | False |
| primary_location.source.display_name | arXiv (Cornell University) |
| primary_location.source.host_organization | https://openalex.org/I205783295 |
| primary_location.source.host_organization_name | Cornell University |
| primary_location.source.host_organization_lineage | https://openalex.org/I205783295 |
| primary_location.license | |
| primary_location.pdf_url | https://arxiv.org/pdf/2406.09638 |
| primary_location.version | submittedVersion |
| primary_location.raw_type | text |
| primary_location.license_id | |
| primary_location.is_accepted | False |
| primary_location.is_published | False |
| primary_location.raw_source_name | |
| primary_location.landing_page_url | http://arxiv.org/abs/2406.09638 |
| publication_date | 2024-06-14 |
| publication_year | 2024 |
| referenced_works_count | 0 |
| abstract_inverted_index.a | 2, 39, 79, 86, 110 |
| abstract_inverted_index.16 | 28 |
| abstract_inverted_index.TB | 29 |
| abstract_inverted_index.We | 0, 101 |
| abstract_inverted_index.an | 60 |
| abstract_inverted_index.be | 66, 119 |
| abstract_inverted_index.in | 30, 82 |
| abstract_inverted_index.of | 18, 41, 85, 94 |
| abstract_inverted_index.to | 14, 68, 77, 114 |
| abstract_inverted_index.100 | 34 |
| abstract_inverted_index.For | 51 |
| abstract_inverted_index.and | 32, 43, 71, 97, 106 |
| abstract_inverted_index.can | 65, 118 |
| abstract_inverted_index.for | 7, 121 |
| abstract_inverted_index.gap | 81 |
| abstract_inverted_index.how | 116 |
| abstract_inverted_index.its | 103 |
| abstract_inverted_index.the | 16, 22, 47, 83, 92 |
| abstract_inverted_index.RASP | 95 |
| abstract_inverted_index.each | 52 |
| abstract_inverted_index.fill | 78 |
| abstract_inverted_index.from | 59 |
| abstract_inverted_index.land | 44 |
| abstract_inverted_index.over | 38 |
| abstract_inverted_index.size | 31 |
| abstract_inverted_index.that | 90 |
| abstract_inverted_index.used | 67, 120 |
| abstract_inverted_index.radar | 8, 24, 62, 70, 124 |
| abstract_inverted_index.types | 45 |
| abstract_inverted_index.which | 64 |
| abstract_inverted_index.(RASP) | 12 |
| abstract_inverted_index.10,000 | 56 |
| abstract_inverted_index.United | 49 |
| abstract_inverted_index.across | 46 |
| abstract_inverted_index.called | 5 |
| abstract_inverted_index.models | 20 |
| abstract_inverted_index.neural | 99 |
| abstract_inverted_index.signal | 10 |
| abstract_inverted_index.within | 21 |
| abstract_inverted_index.RASPNet | 6, 26, 54, 75, 117 |
| abstract_inverted_index.States. | 50 |
| abstract_inverted_index.clutter | 57 |
| abstract_inverted_index.dataset | 4, 89 |
| abstract_inverted_index.example | 113 |
| abstract_inverted_index.exceeds | 27 |
| abstract_inverted_index.intends | 76 |
| abstract_inverted_index.outline | 102 |
| abstract_inverted_index.present | 1 |
| abstract_inverted_index.several | 107 |
| abstract_inverted_index.support | 15 |
| abstract_inverted_index.variety | 40 |
| abstract_inverted_index.adaptive | 9, 23, 123 |
| abstract_inverted_index.airborne | 61 |
| abstract_inverted_index.compiled | 37 |
| abstract_inverted_index.learning | 73, 112 |
| abstract_inverted_index.setting, | 63 |
| abstract_inverted_index.transfer | 111 |
| abstract_inverted_index.benchmark | 69 |
| abstract_inverted_index.comprises | 33, 55 |
| abstract_inverted_index.including | 109 |
| abstract_inverted_index.networks. | 100 |
| abstract_inverted_index.prominent | 80 |
| abstract_inverted_index.realistic | 35, 88 |
| abstract_inverted_index.scenario, | 53 |
| abstract_inverted_index.scenarios | 36 |
| abstract_inverted_index.community. | 25 |
| abstract_inverted_index.contiguous | 48 |
| abstract_inverted_index.evaluation | 93 |
| abstract_inverted_index.processing | 11 |
| abstract_inverted_index.real-world | 122 |
| abstract_inverted_index.scenarios. | 125 |
| abstract_inverted_index.techniques | 96 |
| abstract_inverted_index.algorithms. | 74 |
| abstract_inverted_index.data-driven | 19 |
| abstract_inverted_index.demonstrate | 115 |
| abstract_inverted_index.development | 17 |
| abstract_inverted_index.large-scale | 3 |
| abstract_inverted_index.applications | 13 |
| abstract_inverted_index.availability | 84 |
| abstract_inverted_index.large-scale, | 87 |
| abstract_inverted_index.realizations | 58 |
| abstract_inverted_index.standardizes | 91 |
| abstract_inverted_index.topographies | 42 |
| abstract_inverted_index.applications, | 108 |
| abstract_inverted_index.construction, | 104 |
| abstract_inverted_index.organization, | 105 |
| abstract_inverted_index.complex-valued | 72, 98 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 5 |
| citation_normalized_percentile |