Parameter Estimation of Communication Radar Fusion System Based on Orthogonal Frequency Division Multiplexing Technology Article Swipe
Xiaoguang Hu
,
Rui Zhang
,
Junpeng Cui
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1109/access.2024.3375880
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1109/access.2024.3375880
The development of communication and radar fusion systems is critical to enhancing sensor network security. However, in spectrum resource environments, the technology for processing fusion signals may face limitations due to multi-user access and interference issues. Traditional signal processing algorithms may also be influenced by waveform information. As a result, its implementation is limited in low-noise-ratio settings. Based on the advantages of multiple-input multiple-output technology in the field of communication and radar, in order to better ensure the non-interference between the communication and radar systems, the study introduces the orthogonal theory. It proposes a target detection and parameter estimation method based on orthogonal frequency division multiplexing (OFDM) technology in the signal modulation domain. It utilizes sparse tensor theory to obtain low rank features of signals, transform the dimensions of target parameter problems, and solve tensor rank 1 approximation problems. This can better improve the performance of target parameter estimation. The simulation analysis of the proposed method shows that the average error values of the improved decomposition method in Doppler frequency shift estimation and angle estimation are close to 10–2 and 10–1 respectively. The system runs for less than 5 seconds, and its average target detection accuracy exceeds 96%. The proposed orthogonal frequency division multiplexing technique can provide technical support for sensor networks in wireless communication and IOT networks, efficiently reducing computational complexity and interference among users. This can serve as a valuable reference for enhancing the security of communication radar fusion systems.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/access.2024.3375880
- https://ieeexplore.ieee.org/ielx7/6287639/6514899/10466544.pdf
- OA Status
- gold
- References
- 30
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4392642834
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https://openalex.org/W4392642834Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1109/access.2024.3375880Digital Object Identifier
- Title
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Parameter Estimation of Communication Radar Fusion System Based on Orthogonal Frequency Division Multiplexing TechnologyWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-01-01Full publication date if available
- Authors
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Xiaoguang Hu, Rui Zhang, Junpeng CuiList of authors in order
- Landing page
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https://doi.org/10.1109/access.2024.3375880Publisher landing page
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https://ieeexplore.ieee.org/ielx7/6287639/6514899/10466544.pdfDirect link to full text PDF
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://ieeexplore.ieee.org/ielx7/6287639/6514899/10466544.pdfDirect OA link when available
- Concepts
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Computer science, Orthogonal frequency-division multiplexing, Radar, Electronic engineering, Multiplexing, Telecommunications, Engineering, Channel (broadcasting)Top concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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30Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.theory. | 90 |
| abstract_inverted_index.However, | 15 |
| abstract_inverted_index.accuracy | 195 |
| abstract_inverted_index.analysis | 151 |
| abstract_inverted_index.critical | 9 |
| abstract_inverted_index.division | 104, 202 |
| abstract_inverted_index.features | 122 |
| abstract_inverted_index.improved | 164 |
| abstract_inverted_index.networks | 211 |
| abstract_inverted_index.proposed | 154, 199 |
| abstract_inverted_index.proposes | 92 |
| abstract_inverted_index.reducing | 219 |
| abstract_inverted_index.resource | 18 |
| abstract_inverted_index.seconds, | 189 |
| abstract_inverted_index.security | 236 |
| abstract_inverted_index.signals, | 124 |
| abstract_inverted_index.spectrum | 17 |
| abstract_inverted_index.systems, | 84 |
| abstract_inverted_index.systems. | 241 |
| abstract_inverted_index.utilizes | 114 |
| abstract_inverted_index.valuable | 231 |
| abstract_inverted_index.waveform | 45 |
| abstract_inverted_index.wireless | 213 |
| abstract_inverted_index.detection | 95, 194 |
| abstract_inverted_index.enhancing | 11, 234 |
| abstract_inverted_index.frequency | 103, 169, 201 |
| abstract_inverted_index.networks, | 217 |
| abstract_inverted_index.parameter | 97, 130, 147 |
| abstract_inverted_index.problems, | 131 |
| abstract_inverted_index.problems. | 138 |
| abstract_inverted_index.reference | 232 |
| abstract_inverted_index.security. | 14 |
| abstract_inverted_index.settings. | 56 |
| abstract_inverted_index.technical | 207 |
| abstract_inverted_index.technique | 204 |
| abstract_inverted_index.transform | 125 |
| abstract_inverted_index.advantages | 60 |
| abstract_inverted_index.algorithms | 39 |
| abstract_inverted_index.complexity | 221 |
| abstract_inverted_index.dimensions | 127 |
| abstract_inverted_index.estimation | 98, 171, 174 |
| abstract_inverted_index.influenced | 43 |
| abstract_inverted_index.introduces | 87 |
| abstract_inverted_index.modulation | 111 |
| abstract_inverted_index.multi-user | 31 |
| abstract_inverted_index.orthogonal | 89, 102, 200 |
| abstract_inverted_index.processing | 23, 38 |
| abstract_inverted_index.simulation | 150 |
| abstract_inverted_index.technology | 21, 64, 107 |
| abstract_inverted_index.10–1 | 180 |
| abstract_inverted_index.10–2 | 178 |
| abstract_inverted_index.96%. | 197 |
| abstract_inverted_index.Traditional | 36 |
| abstract_inverted_index.development | 1 |
| abstract_inverted_index.efficiently | 218 |
| abstract_inverted_index.estimation. | 148 |
| abstract_inverted_index.limitations | 28 |
| abstract_inverted_index.performance | 144 |
| abstract_inverted_index.information. | 46 |
| abstract_inverted_index.interference | 34, 223 |
| abstract_inverted_index.multiplexing | 105, 203 |
| abstract_inverted_index.approximation | 137 |
| abstract_inverted_index.communication | 3, 69, 81, 214, 238 |
| abstract_inverted_index.computational | 220 |
| abstract_inverted_index.decomposition | 165 |
| abstract_inverted_index.environments, | 19 |
| abstract_inverted_index.respectively. | 181 |
| abstract_inverted_index.implementation | 51 |
| abstract_inverted_index.multiple-input | 62 |
| abstract_inverted_index.low-noise-ratio | 55 |
| abstract_inverted_index.multiple-output | 63 |
| abstract_inverted_index.non-interference | 78 |
| cited_by_percentile_year | |
| countries_distinct_count | 1 |
| institutions_distinct_count | 3 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/7 |
| sustainable_development_goals[0].score | 0.4399999976158142 |
| sustainable_development_goals[0].display_name | Affordable and clean energy |
| citation_normalized_percentile.value | 0.01664933 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |