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<p>Information-theoretic principles provide a rigorous foundation for adaptive radar waveform design in contested and dynamically varying environments. This paper addresses the joint optimization of constant modulus waveforms to enhance both target detection and parameter estimation concurrently. A unified design framework is developed by maximizing a mutual information upper bound (MIUB), which intrinsically reconciles the tradeoff between detection sensitivity and estimation accuracy without heuristic weighting. Realistic, potentially non-Gaussian statistics of target and clutter returns are modeled using Gaussian mixture distributions (GMDs), enabling tractable closed-form approximations of the MIUB’s Kullback–Leibler divergence and mutual information components. To tackle the ensuing non-convex optimization, a tailored metaheuristic phase-coded dream optimization algorithm (PC-DOA) is proposed, incorporating hybrid initialization and adaptive exploration–exploitation mechanisms for efficient phase-space search. Numerical results substantiate the proposed approach’s superiority in achieving favorable detection estimation trade-offs over existing benchmarks.</p>

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Waveform design based on mutual information upper bound for joint detection and estimation

  • 期刊论文

  • 发表日期：

    2025年11月27日

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  • Ruofeng Yu,
  • Chenyang Luo,
  • Mengdi Bai,
  • Shangqu Yan,
  • Wei Yang,
  • Yaowen Fu

摘要

Information-theoretic principles provide a rigorous foundation for adaptive radar waveform design in contested and dynamically varying environments. This paper addresses the joint optimization of constant modulus waveforms to enhance both target detection and parameter estimation concurrently. A unified design framework is developed by maximizing a mutual information upper bound (MIUB), which intrinsically reconciles the tradeoff between detection sensitivity and estimation accuracy without heuristic weighting. Realistic, potentially non-Gaussian statistics of target and clutter returns are modeled using Gaussian mixture distributions (GMDs), enabling tractable closed-form approximations of the MIUB’s Kullback–Leibler divergence and mutual information components. To tackle the ensuing non-convex optimization, a tailored metaheuristic phase-coded dream optimization algorithm (PC-DOA) is proposed, incorporating hybrid initialization and adaptive exploration–exploitation mechanisms for efficient phase-space search. Numerical results substantiate the proposed approach’s superiority in achieving favorable detection estimation trade-offs over existing benchmarks.