四阶累积量MUSIC算法及其在谐波提取中的应用

The Fourth Order Cumulant MUSIC Algorithm and it's Using in Extracting of Harmonic Characteristics

MUSIC方法具有很好的分辨性能,而四阶累积量具有良好的盲噪声特性,将四阶累积量与MUSIC方法相结合,将能够很好的应用于诸如动态目标回波谐波特征等微弱信号的提取。文中简要说明了四阶累积量MUSIC算法的构造方法,以及为了降低算法的估计方差而进行的Toep litz化预处理方法,详细讨论了谐波个数与谐波振幅对算法分辨性能的影响,认为对于有限的采样数,在一定的信噪比下,谐波数目和谐波振幅对算法的性能存在着一定的影响。文中应用四阶累积量MU-SIC算法对某运动目标的实测回波进行计算,得到了目标回波真实的谐波构造特征,计算结果与目标的运动特性一致。

MUSIC algorithm has good distinguish capability, while the fourth order cumulant has good capabilities to blind noise, so if combine the MUSIC algorithm and the fourth order cumulant, the new algorithm will get a good use in extracting the faintness signal such as dynamic target echo＇s harmonic waves. In this paper, the processing of fourth order cumulant MUSIC algorithm and the method of Toeplitz pretreatment for reduce the algorithm＇s estimate deviation were showed briefly, the effect of the number and amplitude of harmonic waves to the algorithm＇s distinguish capability were discussed particularly, and the result showed that for the limited hits and definite SNR, the number and amplitude of harmonics can influence the algorithm＇s capability in some degrees. In the end, we used the fourth order cumulant MUSIC algorithm to the measured data, and got the actual harmonic properties of one targets＇ echo, the calculate result is consistent with the targets movement characteristics.