DOA estimation algorithm for high maneuvering scenarios Based on improved covariance matrix Article Swipe
Zheng‐Tang Liu
,
Yan-jie CHENG
,
Hui Ma
,
Wen Yang
,
Huimin Gao
,
Dong‐Dong Zhou
·
YOU?
·
· 2016
· Open Access
·
· DOI: https://doi.org/10.2991/icamcs-16.2016.129
YOU?
·
· 2016
· Open Access
·
· DOI: https://doi.org/10.2991/icamcs-16.2016.129
Aiming at the problem that signal snapshot data of the target is sparse and conventional algorithm is difficult to obtain the signal subspace ,a new DOA decorrelation algorithm based on improved covariance matrix is proposed.Firstly, the received single snapshot data is pretreated by cross-correlation , then the equivalent covariance matrix is reconstructed by using the data obtained from the pretreatment, and then the DOA estimation of coherent signals is completed based on the MUSIC algorithm.Without losing the aperture of the array ,the proposed algorithm ensures the accuracy of the spectral estimation .Simulation results demonstrate that the proposed algorithm can achieve better performance than that of traditional method in high-speed mobile scenarios.
Related Topics
Concepts
Decorrelation
Snapshot (computer storage)
Covariance matrix
Algorithm
Computer science
Direction of arrival
Covariance
Subspace topology
Eigendecomposition of a matrix
Mathematics
Artificial intelligence
Eigenvalues and eigenvectors
Statistics
Physics
Telecommunications
Antenna (radio)
Quantum mechanics
Operating system
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.2991/icamcs-16.2016.129
- https://download.atlantis-press.com/article/25855027.pdf
- OA Status
- gold
- References
- 6
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2397862185
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W2397862185Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.2991/icamcs-16.2016.129Digital Object Identifier
- Title
-
DOA estimation algorithm for high maneuvering scenarios Based on improved covariance matrixWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2016Year of publication
- Publication date
-
2016-01-01Full publication date if available
- Authors
-
Zheng‐Tang Liu, Yan-jie CHENG, Hui Ma, Wen Yang, Huimin Gao, Dong‐Dong ZhouList of authors in order
- Landing page
-
https://doi.org/10.2991/icamcs-16.2016.129Publisher landing page
- PDF URL
-
https://download.atlantis-press.com/article/25855027.pdfDirect 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
- OA URL
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https://download.atlantis-press.com/article/25855027.pdfDirect OA link when available
- Concepts
-
Decorrelation, Snapshot (computer storage), Covariance matrix, Algorithm, Computer science, Direction of arrival, Covariance, Subspace topology, Eigendecomposition of a matrix, Mathematics, Artificial intelligence, Eigenvalues and eigenvectors, Statistics, Physics, Telecommunications, Antenna (radio), Quantum mechanics, Operating systemTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- References (count)
-
6Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.from | 57 |
| abstract_inverted_index.than | 102 |
| abstract_inverted_index.that | 4, 94, 103 |
| abstract_inverted_index.then | 45, 61 |
| abstract_inverted_index.MUSIC | 73 |
| abstract_inverted_index.array | 80 |
| abstract_inverted_index.based | 28, 70 |
| abstract_inverted_index.using | 53 |
| abstract_inverted_index.Aiming | 0 |
| abstract_inverted_index.better | 100 |
| abstract_inverted_index.losing | 75 |
| abstract_inverted_index.matrix | 32, 49 |
| abstract_inverted_index.method | 106 |
| abstract_inverted_index.mobile | 109 |
| abstract_inverted_index.obtain | 19 |
| abstract_inverted_index.signal | 5, 21 |
| abstract_inverted_index.single | 37 |
| abstract_inverted_index.sparse | 12 |
| abstract_inverted_index.target | 10 |
| abstract_inverted_index.achieve | 99 |
| abstract_inverted_index.ensures | 84 |
| abstract_inverted_index.problem | 3 |
| abstract_inverted_index.results | 92 |
| abstract_inverted_index.signals | 67 |
| abstract_inverted_index.accuracy | 86 |
| abstract_inverted_index.aperture | 77 |
| abstract_inverted_index.coherent | 66 |
| abstract_inverted_index.improved | 30 |
| abstract_inverted_index.obtained | 56 |
| abstract_inverted_index.proposed | 82, 96 |
| abstract_inverted_index.received | 36 |
| abstract_inverted_index.snapshot | 6, 38 |
| abstract_inverted_index.spectral | 89 |
| abstract_inverted_index.subspace | 22 |
| abstract_inverted_index.algorithm | 15, 27, 83, 97 |
| abstract_inverted_index.completed | 69 |
| abstract_inverted_index.difficult | 17 |
| abstract_inverted_index.covariance | 31, 48 |
| abstract_inverted_index.equivalent | 47 |
| abstract_inverted_index.estimation | 64, 90 |
| abstract_inverted_index.high-speed | 108 |
| abstract_inverted_index.pretreated | 41 |
| abstract_inverted_index.scenarios. | 110 |
| abstract_inverted_index..Simulation | 91 |
| abstract_inverted_index.demonstrate | 93 |
| abstract_inverted_index.performance | 101 |
| abstract_inverted_index.traditional | 105 |
| abstract_inverted_index.conventional | 14 |
| abstract_inverted_index.decorrelation | 26 |
| abstract_inverted_index.pretreatment, | 59 |
| abstract_inverted_index.reconstructed | 51 |
| abstract_inverted_index.algorithm.Without | 74 |
| abstract_inverted_index.cross-correlation | 43 |
| abstract_inverted_index.proposed.Firstly, | 34 |
| cited_by_percentile_year | |
| countries_distinct_count | 1 |
| institutions_distinct_count | 6 |
| citation_normalized_percentile.value | 0.03327457 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |