Cramér-Rao Bound Optimization for Near-Field ISAC with Extended Targets
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arXiv
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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866915945673392128 |
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| author | Zhao, Zongyao Wang, Zhaolin Han, Lincong Xu, Liang Jin, Jing Liu, Yuanwei Huang, Kaibin |
| author_facet | Zhao, Zongyao Wang, Zhaolin Han, Lincong Xu, Liang Jin, Jing Liu, Yuanwei Huang, Kaibin |
| contents | Near-field integrated sensing and communication (ISAC) requires target models beyond the point-target abstraction when the target has a non-negligible spatial extent. In this letter, a geometry-aware transmit design is developed for a parametric extended target (ET) described by its center, orientation, and size under spherical-wave propagation. The CRB for the geometric parameters is formulated around a nominal ET state, an exact ET-aware reduced subspace is identified for the lifted covariance formulation, and a reduced-dimensional semidefinite relaxation (SDR) is developed under signal-to-interference-plus-noise ratio (SINR) and power constraints. Simulation results show lower CRB values than point-target and geometry-agnostic baselines together with substantially reduced runtime for large arrays. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_18166 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Cramér-Rao Bound Optimization for Near-Field ISAC with Extended Targets Zhao, Zongyao Wang, Zhaolin Han, Lincong Xu, Liang Jin, Jing Liu, Yuanwei Huang, Kaibin Signal Processing Near-field integrated sensing and communication (ISAC) requires target models beyond the point-target abstraction when the target has a non-negligible spatial extent. In this letter, a geometry-aware transmit design is developed for a parametric extended target (ET) described by its center, orientation, and size under spherical-wave propagation. The CRB for the geometric parameters is formulated around a nominal ET state, an exact ET-aware reduced subspace is identified for the lifted covariance formulation, and a reduced-dimensional semidefinite relaxation (SDR) is developed under signal-to-interference-plus-noise ratio (SINR) and power constraints. Simulation results show lower CRB values than point-target and geometry-agnostic baselines together with substantially reduced runtime for large arrays. |
| title | Cramér-Rao Bound Optimization for Near-Field ISAC with Extended Targets |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2604.18166 |