Time difference of arrival (TDOA) is essential in localization, communication, and navigation. Under ambient noise interference, the impulsive signal is transmitted over a long distance and reaches the sensor with a low signal-to-noise ratio (SNR). Aiming to achieve a precise time delay estimation in scenarios with low SNR, this paper extends the time-reassigned extracting transform (TRET) theory to TDOA and proposes a time difference rearrangement extracting transform (TDRT) algorithm. The implementation process of the proposed algorithm comprises three steps. Initially, a rough two-dimensional time delay estimation is obtained by calculating the partial derivative of the short-time cross-power spectrum with respect to the frequency variable. Secondly, a rearrangement operation separates the signal's time-frequency (TF) points from the noise. Thirdly, a refined TDOA estimation is obtained by inverse Fourier transforming TF points extracted from the time-delay energy ridge. Simulation results show that the TDRT algorithm is effective in time delay estimation. Furthermore, experimental results prove that the performance of the TDRT algorithm outperforms comparable algorithms in low SNR environments.

中文翻译：

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时差重分配变换在脉冲信号到达时间差中的应用

到达时间差 (TDOA) 在定位、通信和导航中至关重要。在环境噪声干扰下，脉冲信号会长距离传输并以较低的信噪比（SNR）到达传感器。为了实现低信噪比场景下的精确时延估计，该文将时间重新分配提取变换（TRET）理论扩展到TDOA，提出一种时间差重排提取变换（TDRT）算法。所提出算法的实现过程包括三个步骤。首先，通过计算短时互功率谱相对于频率变量的偏导数来获得粗略的二维时间延迟估计。其次，重新排列操作将信号的时频 (TF) 点与噪声分开。第三，通过对从时延能量脊提取的TF点进行逆傅里叶变换，获得精细的TDOA估计。仿真结果表明TDRT算法在时延估计方面是有效的。此外，实验结果证明，在低信噪比环境下，TDRT 算法的性能优于同类算法。