基于奇异值分解的广义互相关时延估计

Generalized Cross Correlation Time Delay Estimation Based on Singular Value Decomposition

广义互相关法是TDOA时延估计中的一种经典方法,为了进一步提升广义互相关时延估计算法在低信噪比下的时延估值准确度,在广义互相关法的基础上,对接收到的信号进行奇异值分解,提升信号的信噪比.仿真结果表明,基于奇异值分解的广义互相关时延估计算法极大地提升了时延估计精度,具有明显的性能优势.

Generalized cross-correlation method is a classical TDOA time delay estimation method.In order to further enhance the generalized cross-correlation time delay estimation algorithm at low signal-noise ratio under the delay estimation accuracy,the signal received at the receiver is singularly-valued decomposed to improve the signal-to-noise ratio of the signal on the basis of the generalized cross-correlation algorithm.The simulation results show that the improved algorithm has good effect on the accuracy of time delay estimation,which has a significant performance advantage.