针对无源时差定位中的稀疏傅里叶变换时延估计算法在低信噪比条件下的抗噪性差和估值精度低等问题, 提出了广义二次相关稀疏傅里叶时延估计算法。算法在对信号进行稀疏傅里叶变换的基础上, 融合利用最小二乘拟合改进的广义二次相关算法, 在对信号进行快速处理的同时抑制了噪声的干扰, 使得时延估计算法的性能得到提高。仿真实验以及对实测数据的验证均表明改进算法具有较好的抗噪性以及时延估值精度。

In order to improve the anti-interference performance and precision of Sparse Fourier Transform (SFT) time delay estimation algorithm under low SNR conditions, a new SFT time delay estimation algorithm is proposed based on Generalized Second Cross Correlation (GSCC).The new algorithm performs the SFT operation on the signal, and then adds the GSCC algorithm improved by least squares fitting.The algorithm rapidly processes the signal while suppressing the interfering noise at the same time.In this way, the performance of the time delay estimation algorithm is improved.Simulation tests and field data verification both indicate that the improved algorithm has satisfying anti-interference performance and estimation precision.