Toward Data-Driven STAP Radar Article Swipe
Shyam Venkatasubramanian
,
Chayut Wongkamthong
,
Mohammadreza Soltani
,
Bosung Kang
,
Sandeep Gogineni
,
Ali Pezeshki
,
Muralidhar Rangaswamy
,
Vahid Tarokh
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2201.10712
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2201.10712
Using an amalgamation of techniques from classical radar, computer vision, and deep learning, we characterize our ongoing data-driven approach to space-time adaptive processing (STAP) radar. We generate a rich example dataset of received radar signals by randomly placing targets of variable strengths in a predetermined region using RFView, a site-specific radio frequency modeling and simulation tool developed by ISL Inc. For each data sample within this region, we generate heatmap tensors in range, azimuth, and elevation of the output power of a minimum variance distortionless response (MVDR) beamformer, which can be replaced with a desired test statistic. These heatmap tensors can be thought of as stacked images, and in an airborne scenario, the moving radar creates a sequence of these time-indexed image stacks, resembling a video. Our goal is to use these images and videos to detect targets and estimate their locations, a procedure reminiscent of computer vision algorithms for object detection$-$namely, the Faster Region-Based Convolutional Neural Network (Faster R-CNN). The Faster R-CNN consists of a proposal generating network for determining regions of interest (ROI), a regression network for positioning anchor boxes around targets, and an object classification algorithm; it is developed and optimized for natural images. Our ongoing research will develop analogous tools for heatmap images of radar data. In this regard, we will generate a large, representative adaptive radar signal processing database for training and testing, analogous in spirit to the COCO dataset for natural images. As a preliminary example, we present a regression network in this paper for estimating target locations to demonstrate the feasibility of and significant improvements provided by our data-driven approach.
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- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.48550/arxiv.2201.10712
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4225752864
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- OpenAlex ID
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https://openalex.org/W4225752864Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.48550/arxiv.2201.10712Digital Object Identifier
- Title
-
Toward Data-Driven STAP RadarWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
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2022-01-26Full publication date if available
- Authors
-
Shyam Venkatasubramanian, Chayut Wongkamthong, Mohammadreza Soltani, Bosung Kang, Sandeep Gogineni, Ali Pezeshki, Muralidhar Rangaswamy, Vahid TarokhList of authors in order
- Landing page
-
https://doi.org/10.48550/arxiv.2201.10712Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.48550/arxiv.2201.10712Direct OA link when available
- Concepts
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Radar, Computer science, Artificial intelligence, Convolutional neural network, Statistic, Computer vision, Radar imaging, Object detection, Pattern recognition (psychology), Range (aeronautics), Deep learning, Mathematics, Engineering, Telecommunications, Aerospace engineering, StatisticsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.RFView, | 47 |
| abstract_inverted_index.creates | 115 |
| abstract_inverted_index.dataset | 30, 234 |
| abstract_inverted_index.desired | 94 |
| abstract_inverted_index.develop | 201 |
| abstract_inverted_index.example | 29 |
| abstract_inverted_index.heatmap | 69, 98, 205 |
| abstract_inverted_index.images, | 106 |
| abstract_inverted_index.images. | 196, 237 |
| abstract_inverted_index.minimum | 82 |
| abstract_inverted_index.natural | 195, 236 |
| abstract_inverted_index.network | 168, 177, 246 |
| abstract_inverted_index.ongoing | 16, 198 |
| abstract_inverted_index.placing | 37 |
| abstract_inverted_index.present | 243 |
| abstract_inverted_index.regard, | 212 |
| abstract_inverted_index.region, | 66 |
| abstract_inverted_index.regions | 171 |
| abstract_inverted_index.signals | 34 |
| abstract_inverted_index.stacked | 105 |
| abstract_inverted_index.stacks, | 122 |
| abstract_inverted_index.targets | 38, 137 |
| abstract_inverted_index.tensors | 70, 99 |
| abstract_inverted_index.thought | 102 |
| abstract_inverted_index.vision, | 9 |
| abstract_inverted_index.adaptive | 21, 219 |
| abstract_inverted_index.airborne | 110 |
| abstract_inverted_index.approach | 18 |
| abstract_inverted_index.azimuth, | 73 |
| abstract_inverted_index.computer | 8, 146 |
| abstract_inverted_index.consists | 163 |
| abstract_inverted_index.database | 223 |
| abstract_inverted_index.estimate | 139 |
| abstract_inverted_index.example, | 241 |
| abstract_inverted_index.generate | 26, 68, 215 |
| abstract_inverted_index.interest | 173 |
| abstract_inverted_index.modeling | 52 |
| abstract_inverted_index.proposal | 166 |
| abstract_inverted_index.provided | 262 |
| abstract_inverted_index.randomly | 36 |
| abstract_inverted_index.received | 32 |
| abstract_inverted_index.replaced | 91 |
| abstract_inverted_index.research | 199 |
| abstract_inverted_index.response | 85 |
| abstract_inverted_index.sequence | 117 |
| abstract_inverted_index.targets, | 183 |
| abstract_inverted_index.testing, | 227 |
| abstract_inverted_index.training | 225 |
| abstract_inverted_index.variable | 40 |
| abstract_inverted_index.variance | 83 |
| abstract_inverted_index.analogous | 202, 228 |
| abstract_inverted_index.approach. | 266 |
| abstract_inverted_index.classical | 6 |
| abstract_inverted_index.developed | 56, 191 |
| abstract_inverted_index.elevation | 75 |
| abstract_inverted_index.frequency | 51 |
| abstract_inverted_index.learning, | 12 |
| abstract_inverted_index.locations | 253 |
| abstract_inverted_index.optimized | 193 |
| abstract_inverted_index.procedure | 143 |
| abstract_inverted_index.scenario, | 111 |
| abstract_inverted_index.strengths | 41 |
| abstract_inverted_index.algorithm; | 188 |
| abstract_inverted_index.algorithms | 148 |
| abstract_inverted_index.estimating | 251 |
| abstract_inverted_index.generating | 167 |
| abstract_inverted_index.locations, | 141 |
| abstract_inverted_index.processing | 22, 222 |
| abstract_inverted_index.regression | 176, 245 |
| abstract_inverted_index.resembling | 123 |
| abstract_inverted_index.simulation | 54 |
| abstract_inverted_index.space-time | 20 |
| abstract_inverted_index.statistic. | 96 |
| abstract_inverted_index.techniques | 4 |
| abstract_inverted_index.beamformer, | 87 |
| abstract_inverted_index.data-driven | 17, 265 |
| abstract_inverted_index.demonstrate | 255 |
| abstract_inverted_index.determining | 170 |
| abstract_inverted_index.feasibility | 257 |
| abstract_inverted_index.positioning | 179 |
| abstract_inverted_index.preliminary | 240 |
| abstract_inverted_index.reminiscent | 144 |
| abstract_inverted_index.significant | 260 |
| abstract_inverted_index.Region-Based | 154 |
| abstract_inverted_index.amalgamation | 2 |
| abstract_inverted_index.characterize | 14 |
| abstract_inverted_index.improvements | 261 |
| abstract_inverted_index.time-indexed | 120 |
| abstract_inverted_index.Convolutional | 155 |
| abstract_inverted_index.predetermined | 44 |
| abstract_inverted_index.site-specific | 49 |
| abstract_inverted_index.classification | 187 |
| abstract_inverted_index.distortionless | 84 |
| abstract_inverted_index.representative | 218 |
| abstract_inverted_index.detection$-$namely, | 151 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 8 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/9 |
| sustainable_development_goals[0].score | 0.4099999964237213 |
| sustainable_development_goals[0].display_name | Industry, innovation and infrastructure |
| citation_normalized_percentile.value | 0.01514018 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |