相敏光时域反射系统异常灵敏,对环境中声波、空气流动及瞬时高频噪音等干扰源同时敏感,环境干扰与入侵非线性混叠时,实际入侵检测与识别困难,容易频繁误报.本文提出一种基于时间序列奇异谱特征的扰动检测方法,对每个滑动时间窗内空间各点的纵向时间序列信号进行相空间重构,对重构后的相空间状态矩阵进行奇异值分解得到信号能量的奇异谱分布,然后将奇异谱特征向量输入后向传播神经网络进行扰动事件检测.实验结果表明,该方法能够有效排除声波及瞬时高频噪音等干扰信号的影响,在微风等有干扰的环境下,在14 km处正确检测率为90%,误警率低于2%.

The phase-sensitive optical time-domain reflectometer systemissensitive to ambient environmentsuch as sound waves, air flow and transient high-frequency noise. Thus the practical signal-to-noise-ratio is always low, the real intrusion signals are always severely obscured and difficult to be detected, as a consequence, false alarm occurs frequently. To solve the problem, a signal processing schemeusing feed-forward neural network together with singular spectrum feature extraction algorithm was presented for disturbance detection. The main points of the method were mapping the slidinglongitudinal time series to a sequence of multi-dimensional lagged vectors which form trajectory matrix and computing the singular value decompositionof the trajectorymatrix. This method can effectively eliminate sound waves and transient high-frequency acoustic noise, etc. Performance results showthat with breeze interference, the correct detection rate is 90% and false alarm rate is less than 2% at a range of 14 km.