摘要
在阵列信号处理中,经典的MUSIC算法是建立在非相干信号模型的基础上的,对于低信噪比条件下的相干多径信号和信源间隔比较近的信号,MUSIC算法难以估计出它们的DOA。利用求根MUSIC算法在小样本空间性能优异的特点,在重构数据协方差矩阵的基础上,通过理论推导,这里给出了一种基于求根MUSIC的改进算法,用于提高低信噪比条件下的相干多径信号与信源间隔比较近的信号DOA谱分辨能力,计算机仿真结果验证了这种方法的有效性.且与修正MUSIC算法相比较,谱分辨能力有明显的提高。
The classical MUSIC algorithm is based on the model of uncorrelated signals in array signal processing, it will be ineffective for both the coherent multi - path signals and adjacent signals with small SNR. Making use of the excellent performance characteristic of root - music algorithm in small sample space, this paper brings forward a modified root -music algorithm based on rebuilding data covariance matrix by deduction to enhance the spectrum resolving power of both coherent multi - path signals and adjacent signals with lower SNR. The computer simulation verifies that it is effective, and its spectrum resolving power is obviously improved compared to modified MUSIC algorithm.
出处
《计算机仿真》
CSCD
2008年第10期340-343,共4页
Computer Simulation
关键词
相干多径
方位估计
数据协方差
谱分辨能力
Coherence multi - path
DOA estimation
Data covariance
Spectrum resolving power
