Roadside Radar Network Deployment and Parameter Optimization in Road Environments
To enable the intelligent transportation systems (ITSs), using radars to help extracting the information of road environments is critical. However, the deployment and parameter optimization of radar networks in practical road environments has not been well-explored yet. To fill this gap, the authors investigate the joint deployment and parameter optimization approach for radar networks in road environments. Considering a general radar network model, the authors first propose a model-based approach developed under some simplifications of the general model. Then, following the optimization framework of the model-based approach and with the aid of black-box optimization, the authors propose a non-model-based approach that can jointly optimize the radar deployment and parameter under the general model without any simplifications. Since conducting the non-model-based approach is time-consuming, the authors further propose a learning-aided approach to accelerate it. The authors use realistic simulations to evaluate our proposed approaches. Results show that our approaches can outperform the reference schemes.
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Availability:
- Find a library where document is available. Order URL: http://worldcat.org/issn/00189545
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Supplemental Notes:
- Copyright © 2024, IEEE.
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Authors:
- Chen, Jian-Kai
- Lee, Ming-Chun
- Kang, Po-Chun
- Lee, Ta-Sung
- Publication Date: 2024-8
Language
- English
Media Info
- Media Type: Web
- Features: References;
- Pagination: pp 11878-11894
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Serial:
- IEEE Transactions on Vehicular Technology
- Volume: 73
- Issue Number: 8
- Publisher: Institute of Electrical and Electronics Engineers (IEEE)
- ISSN: 0018-9545
- Serial URL: http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=25
Subject/Index Terms
- TRT Terms: Intelligent transportation systems; Optimization; Radar; Roadside
- Subject Areas: Data and Information Technology; Highways;
Filing Info
- Accession Number: 01930115
- Record Type: Publication
- Files: TRIS
- Created Date: Sep 13 2024 10:33AM