A Low-Cost Monopulse Receiver with Enhanced Estimation Accuracy Via Deep Neural Network
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arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866909406006870016 |
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| author | Zhang, Hanxiang Pour, Saeed Zolfaghary Yan, Hao Liu, Powei Arigong, Bayaner |
| author_facet | Zhang, Hanxiang Pour, Saeed Zolfaghary Yan, Hao Liu, Powei Arigong, Bayaner |
| contents | In this paper, a low-cost monopulse receiver with an enhanced direction of arrival (DoA) estimation accuracy via deep neural network (DNN) is proposed. The entire system is composed of a 4-element patch array, a fully planar symmetrical monopulse comparator network, and a down conversion link. Unlike the conventional design topology, the proposed monopulse comparator network is configured by four novel port-transformation rat-race couplers. In specific, the proposed coupler is designed to symmetrically allocate the sum (Σ) / delta (Δ) ports with input ports, where a 360° phase delay crossover is designed to transform the unsymmetrical ports in the conventional rat-race coupler. This new rat-race coupler resolves the issues in conventional monopulse receiver comparator network design using multilayer and expensive fabrication technology. To verify the design theory, a prototype of the proposed planar monopulse comparator network operating at 2 GHz is designed, simulated, and measured. In addition, the monopulse radiation patterns and direction of arrival are also decently evaluated. To further boost the accuracy of angular information, a deep neural network is introduced to map the misaligned target angular positions in the measurement to the actual physical location under detection. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_17734 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Low-Cost Monopulse Receiver with Enhanced Estimation Accuracy Via Deep Neural Network Zhang, Hanxiang Pour, Saeed Zolfaghary Yan, Hao Liu, Powei Arigong, Bayaner Signal Processing In this paper, a low-cost monopulse receiver with an enhanced direction of arrival (DoA) estimation accuracy via deep neural network (DNN) is proposed. The entire system is composed of a 4-element patch array, a fully planar symmetrical monopulse comparator network, and a down conversion link. Unlike the conventional design topology, the proposed monopulse comparator network is configured by four novel port-transformation rat-race couplers. In specific, the proposed coupler is designed to symmetrically allocate the sum (Σ) / delta (Δ) ports with input ports, where a 360° phase delay crossover is designed to transform the unsymmetrical ports in the conventional rat-race coupler. This new rat-race coupler resolves the issues in conventional monopulse receiver comparator network design using multilayer and expensive fabrication technology. To verify the design theory, a prototype of the proposed planar monopulse comparator network operating at 2 GHz is designed, simulated, and measured. In addition, the monopulse radiation patterns and direction of arrival are also decently evaluated. To further boost the accuracy of angular information, a deep neural network is introduced to map the misaligned target angular positions in the measurement to the actual physical location under detection. |
| title | A Low-Cost Monopulse Receiver with Enhanced Estimation Accuracy Via Deep Neural Network |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2411.17734 |