用自编码器从噪声信号中精准分离并估计多个衰减正弦波的参数。
Autoencoder-Based Parameter Estimation for Superposed Multi-Component Damped Sinusoidal Signals
- 利用自编码器的隐空间建模多分量衰减正弦信号
- 在快速衰减、低信噪比下仍能高精度估计参数
- 适用于短时、噪声大信号，适合物理系统分析
衰减正弦振荡广泛存在于各类物理系统中，其分析可揭示系统内在特性。然而当信号衰减迅速、多分量叠加且存在观测噪声时，参数估计变得困难。本研究提出一种基于自编码器的方法，通过隐空间对噪声多分量衰减正弦信号中的每个分量的频率、相位、衰减时间与振幅进行估计。我们在高斯分布训练基础上，进一步对比了高斯与均匀分布训练数据的影响。性能通过波形重建与参数估计精度评估。结果表明，该方法在包含次主导分量或近反相分量等复杂场景下仍能实现高精度参数估计，且在训练分布信息较少时仍保持合理鲁棒性。这表明其在分析短时、噪声信号方面具有潜力。
原文摘要 · Abstract (English)
Damped sinusoidal oscillations are widely observed in many physical systems, and their analysis provides access to underlying physical properties. However, parameter estimation becomes difficult when the signal decays rapidly, multiple components are superposed, and observational noise is present. In this study, we develop an autoencoder-based method that uses the latent space to estimate the frequency, phase, decay time, and amplitude of each component in noisy multi-component damped sinusoidal signals. We investigate multi-component cases under Gaussian-distribution training and further examine the effect of the training-data distribution through comparisons between Gaussian and uniform training. The performance is evaluated through waveform reconstruction and parameter-estimation accuracy. We find that the proposed method can estimate the parameters with high accuracy even in challenging setups, such as those involving a subdominant component or nearly opposite-phase components, while remaining reasonably robust when the training distribution is less informative. This demonstrates its potential as a tool for analyzing short-duration, noisy signals.
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