基于二次特征提取的神经网络信号源个数估计方法  

Detection of Number of Sources Based on Secondary Feature Extraction Using Neural Network

作　　者：张兴良[1] 王可人[1] 樊甫华[1] 

机构地区：[1]解放军电子工程学院,合肥230037

出　　处：《火力与指挥控制》2014年第5期13-17,共5页Fire Control & Command Control

基　　金：国家自然科学基金资助项目(61171170)

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。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.

关 键 词：空间谱估计 信源数目 神经网络 特征提取 

分 类 号：TN911.23[电子电信—通信与信息系统]