基于RLS自适应滤波算法的广义互相关时延估计方法研究

为了提高低信噪比环境下信号传输时间延迟的估计性能,将RLS自适应滤波算法与广义互相关时延估计算法相结合,提出了一种基于RLS自适应滤波的广义互相关时延估计(RLS-GCC)方法。该方法先对信号进行滤波预处理以提高信噪比,然后通过广义互相关运算实现时延估计。通过改变RLS自适应滤波算法中的参数大小,分析了RLS自适应滤波算法中滤波阶数、遗传因子等参数对RLS-GCC算法的影响,并通过与广义互相关算法、二次相关算法的对比时延实验,研究了RLS-GCC的时延估计性能。仿真结果表明,在低信噪比环境下,RLS-GCC方法比广义互相关算法和二次相关算法具有更高的时延估计准确率和更低的估计误差。

Research on Generalized Cross-correlation Delay Estimation Method Based on RLS Adaptive Filtering Algorithm

In order to improve the estimation performance of signal transmission time delay in low SNR environment,a generalized cross-correlation time delay estimation method based on RLS adaptive filtering(RLS-GCC method)is proposed by combining RLS adaptive filtering algorithm with generalized cross-correlation time delay estimation algorithm.In this method,the signal is preprocessed by filtering to improve SNR,and then the delay estimation is realized by generalized cross correlation.By changing the size of parameters in the RLS adaptive filtering algorithm,the influence of filtering order,genetic factor and other parameters in the RLS adaptive filtering algorithm on the RLS-GCC algorithm is analyzed,and the time delay estimation performance of RLS-GCC is studied by comparing the time delay experiment with the generalized cross-correlation algorithm and the secondary correlation algorithm.Simulation results show that RLS-GCC method has higher accuracy and lower error than generalized cross correlation algorithm and secondary correlation algorithm in low SNR environment.