数值计算与计算机应用
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数值计算与计算机应用  2018, Vol. 39 Issue (2): 81-90    DOI:
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一种自相关矩阵改进的Pisarenko算法
张晓威, 吕倩倩, 闫会敏, 万旭
哈尔滨工程大学, 哈尔滨 150001
IMPROVED PISARENKO ALGORITHM BASED ON SELF CORRELATION
Zhang Xiaowei, Lü Qianqian, Yan Huimin, Wan Xu
Science School of Harbin Engineering University, Harbin 150001, China
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摘要 Pisarenko算法是通过N个采样数据估计一个平稳随机信号参数的算法.改进的Pisarenko算法在噪声环境下可以精确估计信号频率.该算法通过一个时域累积量等式选择适当的数量关系得到新的自相关矩阵,最后利用最小二乘法求解得到频率的估计值.该算法把频域中提取的信号转换到时域中求解,从而避免了经典Pisarenko算法由于对自相关矩阵进行降维,使用样本自相关函数少,影响最后频率估计精度的缺点.仿真实验结果表明,所提的算法能够有效提高频率估计值的精度,频率估计性能稳定.
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关键词Pisarenko算法   时域累积量   自相关函数   信噪比     
Abstract: Pisarenko is an algorithm to estimate the parameters of a stationary random signal through N sampling data. Improved Pisarenko algorithm can accurately estimate the signal frequency in the noise environment. In this algorithm, a new self correlation matrix is obtained by choosing proper a time domain accumulation quantity relations. Finally, the estimated value of the frequency is obtained by using the least square method. The algorithm estimate the signal frequency which is extracted from the frequency domain in the time domain solution. Due to the classic Pisarenko algorithm is reduce the self correlation matrix dimensionality using sample autocorrelation function less, it avoided this weakness which affected the accuracy of frequency estimation. The simulation results show that the proposed algorithm can effectively improve the accuracy of the frequency estimation, and the frequency estimation performance is stable.
Key wordsPisarenko algorithm   time domain accumulation quantity   self correlation   SNR   
收稿日期: 2017-06-23;
引用本文:   
. 一种自相关矩阵改进的Pisarenko算法[J]. 数值计算与计算机应用, 2018, 39(2): 81-90.
. IMPROVED PISARENKO ALGORITHM BASED ON SELF CORRELATION[J]. Journal of Numerical Methods and Computer Applicat, 2018, 39(2): 81-90.
 
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