A Unified RCS Modeling of Typical Targets for 3GPP ISAC Channel Standardization and Experimental Analysis
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| Main Authors: | , , , , , , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866912396058034176 |
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| author | Zhang, Yuxiang Zhang, Jianhua Hu, Xidong Zhang, Jiwei Xing, Hongbo Gong, Huiwen Luo, Shilin Xiong, Yifeng Yu, Li Yuan, Zhiqing Liu, Guangyi Jiang, Tao |
| author_facet | Zhang, Yuxiang Zhang, Jianhua Hu, Xidong Zhang, Jiwei Xing, Hongbo Gong, Huiwen Luo, Shilin Xiong, Yifeng Yu, Li Yuan, Zhiqing Liu, Guangyi Jiang, Tao |
| contents | Accurate radar cross section (RCS) modeling is crucial for characterizing target scattering and improving the precision of Integrated Sensing and Communication (ISAC) channel modeling. Existing RCS models are typically designed for specific target types, leading to increased complexity and lack of generalization. This makes it difficult to standardize RCS models for 3GPP ISAC channels, which need to account for multiple typical target types simultaneously. Furthermore, 3GPP models must support both system-level and link-level simulations, requiring the integration of large-scale and small-scale scattering characteristics. To address these challenges, this paper proposes a unified RCS modeling framework that consolidates these two aspects. The model decomposes RCS into three components: (1) a large-scale power factor representing overall scattering strength, (2) a small-scale angular-dependent component describing directional scattering, and (3) a random component accounting for variations across target instances. We validate the model through mono-static RCS measurements for UAV, human, and vehicle targets across five frequency bands. The results demonstrate that the proposed model can effectively capture RCS variations for different target types. Finally, the model is incorporated into an ISAC channel simulation platform to assess the impact of target RCS characteristics on path loss, delay spread, and angular spread, providing valuable insights for future ISAC system design. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_20673 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Unified RCS Modeling of Typical Targets for 3GPP ISAC Channel Standardization and Experimental Analysis Zhang, Yuxiang Zhang, Jianhua Hu, Xidong Zhang, Jiwei Xing, Hongbo Gong, Huiwen Luo, Shilin Xiong, Yifeng Yu, Li Yuan, Zhiqing Liu, Guangyi Jiang, Tao Signal Processing Accurate radar cross section (RCS) modeling is crucial for characterizing target scattering and improving the precision of Integrated Sensing and Communication (ISAC) channel modeling. Existing RCS models are typically designed for specific target types, leading to increased complexity and lack of generalization. This makes it difficult to standardize RCS models for 3GPP ISAC channels, which need to account for multiple typical target types simultaneously. Furthermore, 3GPP models must support both system-level and link-level simulations, requiring the integration of large-scale and small-scale scattering characteristics. To address these challenges, this paper proposes a unified RCS modeling framework that consolidates these two aspects. The model decomposes RCS into three components: (1) a large-scale power factor representing overall scattering strength, (2) a small-scale angular-dependent component describing directional scattering, and (3) a random component accounting for variations across target instances. We validate the model through mono-static RCS measurements for UAV, human, and vehicle targets across five frequency bands. The results demonstrate that the proposed model can effectively capture RCS variations for different target types. Finally, the model is incorporated into an ISAC channel simulation platform to assess the impact of target RCS characteristics on path loss, delay spread, and angular spread, providing valuable insights for future ISAC system design. |
| title | A Unified RCS Modeling of Typical Targets for 3GPP ISAC Channel Standardization and Experimental Analysis |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2505.20673 |