Fig. 1. SMS fiber structural diagram

Fig. 2. Structure of convolutional neural network

Fig. 3. Experimental diagrams of pattern recognition in perimeter defense area. (a) Knocking; (b) shaking; (c) winding; (d) raining

Fig. 4. Normalized waveforms of four intrusion signals. (a) Knocking; (b) shaking; (c) winding; (d) raining

Fig. 5. STFT time-frequency diagrams of two kinds of window functions for processing four intrusion events.(a)(c)(e)(g) Time-frequency diagrams of knocking, shaking, winding, and raining signals after passing through the Hanning window;(b)(d)(f)(h) time-frequency diagrams of knocking, shaking, winding, and raining signals after passing through the Kaiser window

Fig. 6. Binarization diagrams of disturbance signals at different resolutions. (a)(c)(e)(g) Time-frequency binarization diagrams of knocking, shaking, winding, and raining signals processed by Kaiser window with window length of 9600; (b)(d)(f)(h) time-frequency binarization diagrams of knocking, shaking, winding, and raining signals processed by Kaiser window with window length of 4800

Fig. 7. Iteration loss diagram of three network models with Hanning window length of 4800

Fig. 8. Recognition rates of Hanning window and Kaisei window with window lengths of 4800 and 9600, respectively

Fig. 9. Time domain diagrams of knocking signal with different Gaussian noise. (a) SNR is 40 dB; (b) SNR is 50 dB; (c) SNR is 60 dB; (d) SNR is 70 dB

Fig. 10. Recognition rates of intrusion signals with different SNR. (a) Knocking; (b) shaking; (c) winding; (d) raining

Fig. 11. Identification results with noise signal

Table 1. Parameters and test results of different network models

Table 2. Comparison of parameters of different input data formats

Table 3. Identification results of artificial and non-human intrusion signals