采用传统单路传感光纤实现对信息特征的测量，常由于交叉敏感等不可控因素使测量数据出现异常值，导致信息分析偏差较大，识别准确度低。因此，提出了一种基于双相干谱、样本熵和奇异值分解(BSS)和反向传播神经网络(BPNNDS)算法的多路光纤入侵传感系统信息的特征提取与识别方法。针对含3路传感光纤的布里渊光时域反射(BOTDR)传感入侵检测系统，该方法利用BSS算法分别对不同入侵类型的多路信号进行特征提取；采用BPNN算法对不同入侵振动特征矢量进行分类；经Dempster Shafer（DS）证据理论算法对多路传感光纤的时空信息进行融合。数值分析与仿真实验结果表明，提出的信息提取方法可以有效提取出多路传感网络的信息特征，且使用BPNN-DS 证据理论的多路信息融合方法能够准确识别多路入侵传感网络的信号类型，具有较高的准确度和可信度。

Because of the cross sensitivity and other uncontrollable factors, the information data appear abnormal and result in the large deviation of information analysis and low recognition accuracy when using traditional monochannel optical sensing fiber to achieve the measurement. A feature extraction and recognition method based on bicoherence spectrum, sample entropy and singular value decomposition (BSS) and back propagation neural network (BPNN)-Dempster Shafer(DS) is proposed. Assuming the intrusion detection system contains three optic sensing fibers based on the Brillouin optical time domain reflection (BOTDR), the method utilizes the BSS algorithm to extract the different intrusion features of multiplex signal, respectively. The classification of the feature vectors for different intrusion vibrations is realized by using the BPNN algorithm and the spatio-temporal information fusion of multi sensing fibers is acquired by Dempster Shafer (DS) evidence theory. Numerical analysis and simulation results show that the novel method can effectively extract the information characteristics of multi-channel sensor networks and have higher accuracy and credibility based on BPNN-DS evidence theory compared with the monochannel optical sensing fiber. This multi-channel information fusion algorithm can also identify signal types of multiintrusion sensor networks accurately.