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| Autores principales: | , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2504.21322 |
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| _version_ | 1866912354586853376 |
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| author | Yu, Ruofeng Zhang, Caiguang Luo, Chenyang Bai, Mengdi Yan, Shangqu Yang, Wei Fu, Yaowen |
| author_facet | Yu, Ruofeng Zhang, Caiguang Luo, Chenyang Bai, Mengdi Yan, Shangqu Yang, Wei Fu, Yaowen |
| contents | Adaptive radar waveform design grounded in information-theoretic principles is critical for advancing cognitive radar performance in complex environments. This paper investigates the optimization of phase-coded waveforms under constant modulus constraints to jointly enhance target detection and parameter estimation. We introduce a unified design framework based on maximizing a Mutual Information Upper Bound (MIUB), which inherently reconciles the trade-off between detection sensitivity and estimation precision without relying on ad hoc weighting schemes. To model realistic, potentially non-Gaussian statistics of target returns and clutter, we adopt Gaussian Mixture Distributions (GMDs), enabling analytically tractable approximations of the MIUB's constituent Kullback-Leibler divergence and mutual information terms. To address the resulting non-convex problem, we propose the Phase-Coded Dream Optimization Algorithm (PC-DOA), a tailored metaheuristic that leverages hybrid initialization and adaptive exploration-exploitation mechanisms specifically designed for phase-variable optimization. Numerical simulations demonstrate the effectiveness of the proposed method in achieving modestly better detection-estimation trade-off. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_21322 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Waveform Design Based on Mutual Information Upper Bound For Joint Detection and Estimation Yu, Ruofeng Zhang, Caiguang Luo, Chenyang Bai, Mengdi Yan, Shangqu Yang, Wei Fu, Yaowen Signal Processing Adaptive radar waveform design grounded in information-theoretic principles is critical for advancing cognitive radar performance in complex environments. This paper investigates the optimization of phase-coded waveforms under constant modulus constraints to jointly enhance target detection and parameter estimation. We introduce a unified design framework based on maximizing a Mutual Information Upper Bound (MIUB), which inherently reconciles the trade-off between detection sensitivity and estimation precision without relying on ad hoc weighting schemes. To model realistic, potentially non-Gaussian statistics of target returns and clutter, we adopt Gaussian Mixture Distributions (GMDs), enabling analytically tractable approximations of the MIUB's constituent Kullback-Leibler divergence and mutual information terms. To address the resulting non-convex problem, we propose the Phase-Coded Dream Optimization Algorithm (PC-DOA), a tailored metaheuristic that leverages hybrid initialization and adaptive exploration-exploitation mechanisms specifically designed for phase-variable optimization. Numerical simulations demonstrate the effectiveness of the proposed method in achieving modestly better detection-estimation trade-off. |
| title | Waveform Design Based on Mutual Information Upper Bound For Joint Detection and Estimation |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2504.21322 |