DOA estimation based on fractional low-order multi-sensor time-frequency analysis in heavy tailed noise Article Swipe
Jing Xu
,
Bin Sun
,
Yang Cao
,
Wei Hong
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1088/1742-6596/1812/1/012007
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1088/1742-6596/1812/1/012007
In order to improve the performance of Direction-of-arrival (DOA) estimation in heavy-tailed noise, a novel DOA estimation method based on fractional low-order multi-sensor time-frequency distribution is proposed. We first use the high-resolution of the fractional low-order multi-sensor time-frequency analysis (FLOM-MTFA) to obtain the time-frequency matrices of the array signal. Then, the high-energy points of the fractional low-order multi-sensor time-frequency matrices is selected to improve the MSNR. We finally compute the averaged FLOM-MTFA matrix and exploit estimation signal parameter via rotational invariance techniques to estimate DOA. Theoretical analysis and simulation results show that the proposed method can effectively estimate DOA in heavy tailed noise.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1088/1742-6596/1812/1/012007
- OA Status
- diamond
- Cited By
- 3
- References
- 17
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3132830261
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- OpenAlex ID
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https://openalex.org/W3132830261Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1088/1742-6596/1812/1/012007Digital Object Identifier
- Title
-
DOA estimation based on fractional low-order multi-sensor time-frequency analysis in heavy tailed noiseWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-02-01Full publication date if available
- Authors
-
Jing Xu, Bin Sun, Yang Cao, Wei HongList of authors in order
- Landing page
-
https://doi.org/10.1088/1742-6596/1812/1/012007Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
- OA URL
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https://doi.org/10.1088/1742-6596/1812/1/012007Direct OA link when available
- Concepts
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Direction of arrival, Noise (video), Algorithm, Computer science, SIGNAL (programming language), Energy (signal processing), Time–frequency analysis, Mathematics, Statistics, Telecommunications, Artificial intelligence, Radar, Antenna (radio), Image (mathematics), Programming languageTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
3Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 1, 2023: 1, 2022: 1Per-year citation counts (last 5 years)
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17Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.noise. | 103 |
| abstract_inverted_index.obtain | 42 |
| abstract_inverted_index.points | 53 |
| abstract_inverted_index.signal | 77 |
| abstract_inverted_index.tailed | 102 |
| abstract_inverted_index.compute | 69 |
| abstract_inverted_index.exploit | 75 |
| abstract_inverted_index.finally | 68 |
| abstract_inverted_index.improve | 4, 64 |
| abstract_inverted_index.results | 90 |
| abstract_inverted_index.signal. | 49 |
| abstract_inverted_index.Abstract | 0 |
| abstract_inverted_index.analysis | 39, 87 |
| abstract_inverted_index.averaged | 71 |
| abstract_inverted_index.estimate | 84, 98 |
| abstract_inverted_index.matrices | 45, 60 |
| abstract_inverted_index.proposed | 94 |
| abstract_inverted_index.selected | 62 |
| abstract_inverted_index.FLOM-MTFA | 72 |
| abstract_inverted_index.low-order | 22, 36, 57 |
| abstract_inverted_index.parameter | 78 |
| abstract_inverted_index.proposed. | 27 |
| abstract_inverted_index.estimation | 10, 17, 76 |
| abstract_inverted_index.fractional | 21, 35, 56 |
| abstract_inverted_index.invariance | 81 |
| abstract_inverted_index.rotational | 80 |
| abstract_inverted_index.simulation | 89 |
| abstract_inverted_index.techniques | 82 |
| abstract_inverted_index.(FLOM-MTFA) | 40 |
| abstract_inverted_index.Theoretical | 86 |
| abstract_inverted_index.effectively | 97 |
| abstract_inverted_index.high-energy | 52 |
| abstract_inverted_index.performance | 6 |
| abstract_inverted_index.distribution | 25 |
| abstract_inverted_index.heavy-tailed | 12 |
| abstract_inverted_index.multi-sensor | 23, 37, 58 |
| abstract_inverted_index.time-frequency | 24, 38, 44, 59 |
| abstract_inverted_index.high-resolution | 32 |
| abstract_inverted_index.Direction-of-arrival | 8 |
| cited_by_percentile_year.max | 95 |
| cited_by_percentile_year.min | 89 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 4 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/7 |
| sustainable_development_goals[0].score | 0.6600000262260437 |
| sustainable_development_goals[0].display_name | Affordable and clean energy |
| citation_normalized_percentile.value | 0.5214304 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |