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<p>Common support multiple exponentially damped sinusoids (CS-MEDS) signals are extensively encountered in the area of sonar, radar, and music synthesis. Existing parameter estimation methods are based on Nyquist sampling theory, which requires a high sampling rate for wide-band signals. In this study, a novel distributed sub-Nyquist sampling and parameter estimation method is presented for CS-MEDS signals. To address the presence of unknown frequency components, a distributed parallel sub-Nyquist sampling framework is constructed. By integrating with a signal subspace-based algorithm, the proposed approach significantly reduces the sampling complexity, while ensuring accurate estimation of frequency components and complex amplitudes. However, sub-Nyquist sampling of CS-MEDS signals introduces challenges such as frequency ambiguity and image aliasing. To resolve these issues, a feedback-enabled sub-Nyquist structure combined with a joint estimation algorithm is designed, enabling accurate parameter extraction. Finally, a hardware platform is subsequently developed to implement and evaluate the proposed system. Both simulation results and hardware experiments confirm the effectiveness of the method, demonstrating accurate parameter estimation under sub-Nyquist sampling conditions.</p>

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Sub-Nyquist Sampling of Common Support MEDS Signals with Feedback Structure

  • 期刊论文

  • 发表日期：

    2025年8月21日

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  • Jingwen Wang,
  • Shibiao Deng,
  • Yunfei Xiang,
  • Yu Zhang,
  • Weidang Lu,
  • Guoxing Huang

摘要

Common support multiple exponentially damped sinusoids (CS-MEDS) signals are extensively encountered in the area of sonar, radar, and music synthesis. Existing parameter estimation methods are based on Nyquist sampling theory, which requires a high sampling rate for wide-band signals. In this study, a novel distributed sub-Nyquist sampling and parameter estimation method is presented for CS-MEDS signals. To address the presence of unknown frequency components, a distributed parallel sub-Nyquist sampling framework is constructed. By integrating with a signal subspace-based algorithm, the proposed approach significantly reduces the sampling complexity, while ensuring accurate estimation of frequency components and complex amplitudes. However, sub-Nyquist sampling of CS-MEDS signals introduces challenges such as frequency ambiguity and image aliasing. To resolve these issues, a feedback-enabled sub-Nyquist structure combined with a joint estimation algorithm is designed, enabling accurate parameter extraction. Finally, a hardware platform is subsequently developed to implement and evaluate the proposed system. Both simulation results and hardware experiments confirm the effectiveness of the method, demonstrating accurate parameter estimation under sub-Nyquist sampling conditions.