用曲率引导的采样法，精准估计高阶啁啾信号参数。
Estimating Multi-chirp Parameters using Curvature-guided Langevin Monte Carlo
- 基于目标函数曲率改进Langevin采样，提升优化稳定性。
- 在低信噪比下仍能准确估计参数，成功率显著提升。
- 适合雷达、声呐等实际场景中的高阶啁啾信号处理。
本文研究从噪声混合啁啾信号中估计啁啾参数的问题。尽管该领域已有大量工作，但在处理高阶多项式啁啾时仍面临挑战。我们将问题建模为非凸优化，并提出一种改进的Langevin Monte Carlo（LMC）采样器，利用目标函数的平均曲率来可靠地找到最小值。结果表明，所提出的曲率引导LMC（CG-LMC）算法具有鲁棒性，在低信噪比（SNR）条件下依然有效，具备实际应用潜力。
原文摘要 · Abstract (English)
This paper considers the problem of estimating chirp parameters from a noisy mixture of chirps. While a rich body of work exists in this area, challenges remain when extending these techniques to chirps of higher order polynomials. We formulate this as a non-convex optimization problem and propose a modified Langevin Monte Carlo (LMC) sampler that exploits the average curvature of the objective function to reliably find the minimizer. Results show that our Curvature-guided LMC (CG-LMC) algorithm is robust and succeeds even in low SNR regimes, making it viable for practical applications.
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