Non-parametric Vibration-based Structural Damage Detection for Coastal Structures: Multi-Dimension to Single Input Convolutional Neural Network Approach
| Autor: | DANG, X.-K., CORCHADO, J. M., LE, V.-V., DO, V.-D. |
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| Jazyk: | angličtina |
| Rok vydání: | 2024 |
| Předmět: | |
| Zdroj: | Advances in Electrical and Computer Engineering, Vol 24, Iss 4, Pp 3-18 (2024) |
| Druh dokumentu: | article |
| ISSN: | 1582-7445 1844-7600 |
| DOI: | 10.4316/AECE.2024.04001 |
| Popis: | Based on multi-station spatial diversity capability, GEO-UAV distributed radar could achieve high-precision aerial target localization with the single-transmitting and multiple-receiving configuration. However, the actual observation area can hardly be covered by several receiving stations simultaneously. Thus, it is necessary to explore a novel target localization method under a single receiving station condition. In this manuscript, an aerial target localization method with GEO-UAV bistatic configuration is presented, where ТOА and AOA measurements are employed. Firstly, measurement models, including bistatic range-delay, pitching AOA, and azimuth AOA, are established using the spatial geometric relationship between the bistatic radar and the target. Then, the receiving range can be estimated using digital beamforming technology based on the receiving array antenna, where the antenna beam coverage information and the prior target altitude information are combined. Finally, the three-dimensional target localization is skillfully derived according to the bistatic configuration, and thus to avoid the parameter unrecognizable problem caused by insufficient degrees of freedom. The proposed algorithm fully exploits the intrinsic correlation characteristics between the measurement information and the bistatic configuration, which provides an effective way for aerial target localization. Simulation results verify the effectiveness of the proposed algorithm. |
| Databáze: | Directory of Open Access Journals |
| Externí odkaz: |
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