提出新方法提升噪声下时差估计精度，适合低信噪比环境使用。
Noise Suppression for Time Difference of Arrival: Performance Evaluation of a Generalized Cross-Correlation Method Using Mean Signal and Inverse Filter
- 用多通道均值信号与逆滤波重建源信号，自适应抑制带外噪声。
- 低信噪比下性能显著优于传统方法，接近或超过最大似然法。
- 阵元数越多，估计精度越优，适用于实际盲区被动定位。
本文提出一种新型广义互相关（GCC）方法——GCC-MSIF，以提升噪声环境下时差到达（TDOA）估计的准确性。传统GCC方法在低信噪比（SNR）条件下性能下降明显，尤其当信号带宽未知时更为严重。GCC-MSIF通过多通道输入估计“均值信号”，结合“逆滤波”虚拟重建源信号，实现对带外噪声的自适应抑制。数值仿真基于小型阵列，结果表明：在低SNR区域，GCC-MSIF显著优于传统方法（如GCC-PHAT和GCC-SCOT），且性能接近甚至超越最大似然法（GCC-ML）。此外，估计精度随阵元数量增加而可扩展提升。这些结果表明，GCC-MSIF是实际盲区中鲁棒被动定位的有力解决方案。
原文摘要 · Abstract (English)
This paper proposes a novel generalized cross-correlation (GCC) method, termed GCC-MSIF, to improve time difference of arrival (TDOA) estimation accuracy in noisy environments. Conventional GCC methods often suffer from performance degradation under low signal-to-noise ratio (SNR) conditions, particularly when the signal bandwidth is unknown. GCC-MSIF introduces a "mean signal" estimated from multi-channel inputs and an "inverse filter" to virtually reconstruct the source signal, enabling adaptive suppression of out-of-band noise. Numerical simulations simulating a small-scale array demonstrate that GCC-MSIF significantly outperforms conventional methods, such as GCC-PHAT and GCC-SCOT, in low SNR regions and achieves robustness comparable to or exceeding the maximum likelihood (GCC-ML) method. Furthermore, the estimation accuracy improves scalably with the number of array elements. These results suggest that GCC-MSIF is a promising solution for robust passive localization in practical blind environments.
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