一种基于二特征提取的神经网络信号源个数估计方法.docVIP

一种基于二次特征提取的神经网络信号源个数估计方法( 张兴良* 王可人 樊甫华 （电子工程学院信息工程系，合肥 230037） 摘要：信源数目估计对阵列信号空间谱估计非常关键，但容易受到相关噪声环境和相关信号源的影响。为此，提出一种基于二次特征提取的源数估计算法。首先，利用阵列信号协方差矩阵的特征值和特征向量，提取6组二次特征参数；然后，利用这些参数对神经网络进行训练；最后，利用训练好的神经网络进行信源数目估计。由于稳健性强，该算法非常适合在复杂电磁环境下使用。仿真试验结果表明，该算法在低信噪比、相关噪声和相关信号源条件下均具有良好的估计性能。因此，该算法应用前景广阔。 关键字：空间谱估计；信源数目；神经网络；特征提取 中图分类号：TN911.23 文献标识码：A Detection of the Number of Sources Based on Secondary Feature Extraction Using Neural Network Zhang Xingliang Wang Keren Fan Fu-hua (Department of Information Engineering, Electronic Engineering Institute, Hefei 230037, China) Abstract: Detection of the number of sources is extremely essential to spatial spectrum estimation in array signal processing. However, it is vulnerable to the correlated noise and the coherent sources fields. Aiming at resolving above problem, a new algorithm for detection of the number of sources based on secondary feature Extraction is proposed. Firstly, six sets of secondary feature parameters are calculated with the eigenvalues and the eigenvectors of the array signal covariance matrix. Then, these parameters are used to train neural network. Last, the number of sources is detected by the trained neural network. The new algorithm is suitable to work in complex electromagnetic environment, for it has high robustness. The results of simulation experiments show that the new algorithm has good performance in the low signal-to-noise ratios, the correlated noise and the coherent sources fields. Therefore, the new algorithm has a wide application prospect. Key words: Spatial spectrum estimation; Number of sources; Neural network; Feature extraction 1 引言 20世纪70年代以来，以MUSIC算法[1]为代表的子空间类算法突破“瑞利限”的限制，实现了谱估计理论的重大飞跃。目前这类算法已广泛应用在雷达、通信和遥感等诸多领域，子空间类算法的优点早已在实践中得到证明。子空间类算法需要以信号源数目已知为前提，但在实际工程中信号源数目信息往往难以直接得到，需要通过算法进行估计。如果信号源数目估计不准确将导致空间谱估计失效，因此准确估计信号源数目对空间谱估计算法至关重要。 目前，主要的信号源数目估计算法有假设检验类算法[2-3]、特征向量法[4-5]、信息论类算法[6]、特征值门限法[7]、盖尔圆准则法[8]等。这些算法都假设背景噪声为白噪声，即阵元间受到的噪声干扰相互独立，然而当背景噪声为空间相关色噪声时，各阵元接收到的噪声不再独立，这些算法的估计性能会下降，