现有的采样率偏移(Sampling Rate Offset,SRO)估计算法在低信噪比条件下性能严重下降.为了解决这个问题,本文提出一种基于子带二次互相关函数的频率滑动窗二次互相关(Frequency Sliding Double-Cross Correlation Processing,FS-DXCP)算法.该方法使用频域滑动窗口构建无线节点观测信号间的子带二次互相关函数矩阵,进而利用奇异值分解来自适应地消除低信噪比频段对二次互相关函数估计的影响,最后搜寻二次互相关函数的极大值点获得SRO的估计.计算机仿真实验表明:在信噪比为-5dB时,所提方法的采样率频偏平均估计误差为4.21百万分率(part per million,ppm),这比现有的DXCP-PHAT算法的估计误差降低了约8.17 ppm.所提算法有效提升了低信噪比条件下采样率频偏的估计精度.
Abstract
The performance of existing sampling rate offset (SRO) estimation algorithms can be degraded significant-ly in low signal-to-noise ratio (SNR) conditions. To address this problem,we propose the frequency-sliding double-cross correlation processing (FS-DXCP) algorithm based on the subband secondary generalized cross-correlation function to esti-mate SRO. The proposed algorithm adopts a frequency-domain sliding window to construct the subband SGCC function ma-trix of the sensor signals. Then,by utilizing the singular value decomposition (SVD),we adaptively mitigate the influence of low SNR frequency bins on estimating secondary generalized cross-correlation functions. Finally,a higher precision SRO estimation is achieved by tracking the maximum point of the estimated SGCC function. Computer simulations show that the root mean squared error of the proposed method for sampling rate offset is 4.21 ppm when the SNR is-5dB,which is about 8.17 ppm lower than that of the double-cross correlation processing with phase transform (DXCP-PHAT) algo-rithm. The proposed algorithm effectively improves the estimation accuracy of the SRO in low SNR conditions.
维普
万方数据