Assessment of Techniques for Detection of Transient Radio-Frequency Interference (RFI) Signals: A Case Study of a Transient in Radar Test Data Article Swipe
Stephen L. Durden
,
V. Vilnrotter
,
S. Shaffer
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/eng4030126
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/eng4030126
The authors present a case study of the investigation of a transient signal that appeared in the testing of a radar receiver. The characteristics of the test conditions and data are first discussed. The authors then proceed to outline the methods for detecting and analyzing transients in the data. For this, they consider several methods based on modern signal processing and evaluate their utility. The initial method used for identifying transients is based on computer vision techniques, specifically, thresholding spectrograms into binary images, morphological processing, and object boundary extraction. The authors also consider deep learning methods and methods related to optimal statistical detection. For the latter approach, since the transient in this case was chirp-like, the method of maximum likelihood is used to estimate its parameters. Each approach is evaluated, followed by a discussion of how the results could be extended to analysis and detection of other types of transient radio-frequency interference (RFI). The authors find that computer vision, deep learning, and statistical detection methods are all useful. However, each is best used at different stages of the investigation when a transient appears in data. Computer vision is particularly useful when little is known about the transient, while traditional statistically optimal detection can be quite accurate once the structure of the transient is known and its parameters estimated.
Related Topics
Concepts
Transient (computer programming)
Thresholding
Computer science
Radar
Artificial intelligence
Interference (communication)
Spectrogram
SIGNAL (programming language)
Pattern recognition (psychology)
Signal processing
Computer vision
Telecommunications
Image (mathematics)
Channel (broadcasting)
Programming language
Operating system
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/eng4030126
- https://www.mdpi.com/2673-4117/4/3/126/pdf?version=1692615116
- OA Status
- gold
- Cited By
- 1
- References
- 26
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4386026655
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4386026655Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/eng4030126Digital Object Identifier
- Title
-
Assessment of Techniques for Detection of Transient Radio-Frequency Interference (RFI) Signals: A Case Study of a Transient in Radar Test DataWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-08-21Full publication date if available
- Authors
-
Stephen L. Durden, V. Vilnrotter, S. ShafferList of authors in order
- Landing page
-
https://doi.org/10.3390/eng4030126Publisher landing page
- PDF URL
-
https://www.mdpi.com/2673-4117/4/3/126/pdf?version=1692615116Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
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https://www.mdpi.com/2673-4117/4/3/126/pdf?version=1692615116Direct OA link when available
- Concepts
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Transient (computer programming), Thresholding, Computer science, Radar, Artificial intelligence, Interference (communication), Spectrogram, SIGNAL (programming language), Pattern recognition (psychology), Signal processing, Computer vision, Telecommunications, Image (mathematics), Channel (broadcasting), Programming language, Operating systemTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
1Total citation count in OpenAlex
- Citations by year (recent)
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2024: 1Per-year citation counts (last 5 years)
- References (count)
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26Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| primary_location.pdf_url | https://www.mdpi.com/2673-4117/4/3/126/pdf?version=1692615116 |
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| primary_location.raw_type | journal-article |
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| primary_location.is_published | True |
| primary_location.raw_source_name | Eng |
| primary_location.landing_page_url | https://doi.org/10.3390/eng4030126 |
| publication_date | 2023-08-21 |
| publication_year | 2023 |
| referenced_works | https://openalex.org/W2079358389, https://openalex.org/W2105556978, https://openalex.org/W2167220975, https://openalex.org/W1965439067, https://openalex.org/W3004340854, https://openalex.org/W3179847226, https://openalex.org/W4256260915, https://openalex.org/W2152294669, https://openalex.org/W6734763211, https://openalex.org/W2558756740, https://openalex.org/W4364302104, https://openalex.org/W4309852602, https://openalex.org/W2987804897, https://openalex.org/W2966046651, https://openalex.org/W2964158751, https://openalex.org/W3037879526, https://openalex.org/W3167854433, https://openalex.org/W4300852401, https://openalex.org/W3046260132, https://openalex.org/W2123913800, https://openalex.org/W2119347354, https://openalex.org/W3156177116, https://openalex.org/W1966032405, https://openalex.org/W2143521914, https://openalex.org/W2110911827, https://openalex.org/W2593331508 |
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| corresponding_institution_ids | https://openalex.org/I1334627681 |
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