发表机构
Korea Advanced Institute of Science and Technology; Korea University（韩国科学技术院; 高丽大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出信噪比自适应频率估计器（SAFE），结合时频图像神经网络和信噪比选择器，在宽信噪比范围内兼顾低信噪比鲁棒性与高信噪比精度，显著提升频率估计性能。
AI 中文摘要
频率估计是从含噪多音正弦信号中估计单个音调频率的问题。现有的频率估计方法在低信噪比（SNR）环境下难以准确估计音调频率的数量和单个音调频率，因为弱音调频率分量被噪声淹没。此外，现有方法通常在低信噪比下的鲁棒性和高信噪比下的频率估计精度之间存在权衡，难以在宽信噪比范围内实现一致优越的频率估计性能。为克服这些限制，本文提出了一种信噪比自适应频率估计器（SAFE）。SAFE由时频图像神经网络（TFINet）和基于信噪比的频率选择器（SFS）组成，前者在低信噪比下增强弱音调频率分量，后者根据估计音调频率的信噪比选择合适的频率估计器。TFINet即使在低信噪比范围内也能增强音调频率分量，而SFS估计每个音调频率的信噪比，并根据估计的信噪比选择鲁棒频率估计器或超分辨率频率估计器。这使得SAFE能够在低信噪比下实现鲁棒性，同时在高信噪比下保持高精度。仿真结果表明，在-10 dB至0 dB的信噪比范围内，SAFE的假阴性率（FNR）达到13.00%，相比最先进方法提高了13.04%。此外，与最先进方法相比，SAFE将最近邻均方根误差（NN-RMSE）降低了56.67%，表明SAFE执行了更准确的频率估计。此外，使用真实世界数据的实验表明，即使在有杂波的实际环境中，SAFE也能提供鲁棒的频率估计性能。
英文摘要
Frequency estimation is the problem of estimating individual tone frequencies from noisy multi-tone sinusoidal signals. Existing frequency estimation methods have difficulty accurately estimating both the number of tone frequencies and the individual tone frequencies in low signal-to-noise ratio (SNR) environments, because weak tone frequency components are buried in noise. In addition, existing methods generally exhibit a trade-off between robustness at low SNR and frequency estimation precision at high SNR, making it difficult to achieve consistently superior frequency estimation performance over a wide SNR range. To overcome these limitations, this paper proposes an SNR-adaptive frequency estimator (SAFE). SAFE consists of a time-frequency image neural network (TFINet), which enhances weak tone frequency components at low SNR, and an SNR-based frequency selector (SFS), which selects an appropriate frequency estimator according to the SNR of the estimated tone frequencies. TFINet enhances tone frequency components even in the low-SNR range, while SFS estimates the SNR of each tone frequency and selects either a robust frequency estimator or a super-resolution frequency estimator according to the estimated SNR. This enables SAFE to achieve robustness at low SNR while preserving high precision at high SNR. Simulation results show that SAFE achieves an False Negative Rate (FNR) of 13.00% over the SNR range from -10 dB to 0 dB, corresponding to an 13.04% improvement over the state-of-the-art method. In addition, SAFE reduces the Nearest Neighbor-Root Mean Squared Error (NN-RMSE) by 56.67% compared with the state-of-the-art method, demonstrating that SAFE performs more accurate frequency estimation. Furthermore, experiments using real-world data demonstrate that SAFE provides robust frequency estimation performance even in practical environments with clutter.
Comments10 pages, 6 figures
