信号源数检测算法研究

Research on Detection Algorithm of Number of Signal Sources

信号处理中波达方向估计在雷达、声呐和移动通信等领域中具有广泛的应用,其中最具代表性的高分辨子空间算法都是在基于信号源数量已知的情况下进行的,因此许多目标源数检测的算法不断被提出。以均匀线阵为例,在分析阵列接收数据协方差矩阵特征值的基础上,通过引入无监督学习中的聚类算法,完成对协方差矩阵特征值的分离,从而有效地检测信号源数。仿真结果表明,本文算法在较低的信噪比和较小的快拍数下相对传统算法具有很高的检测精度。

The direction of arrival （DOA） estimation in signal processing is widely used in radar, sonar and mobile communication. The most representative high-resolution subspace algorithm is based on the known number of the signal source. Thus, many target source number detection algorithms have been constantly proposed. In this paper, we take the unitbrm linear array as the example. Based on the analysis of the eigenvalues of array data covariance matrix, the eigenvalues of covariance matrix will be separated by introducing the clustering algorithm contained in unsupervised learning. Finally, the number of signal sources is effectively detected. The simulation results show that compared to traditional algorithms, our algorithm has a high detection accuracy in the condition of low signal-to- noise ratio （SNR） and small number of snapshots.