Research On Signal Analysis Technology Of Optical Fiber Perimeter Security System Based On Deep Learning

At present,the demand for perimeter security systems at home and abroad is increasing day by day,and various perimeter security technologies have gradually been widely used.Among them,the optical fiber perimeter security system uses optical fiber sensors to collect vibration signals.With the sensitivity of the optical fiber to the vibration and pressure acting on it,it can effectively detect intrusion events such as climbing,knocking,and digging;at the same time,because of its ability to prevent large range,anti-electromagnetic interference and other characteristics,occupy a certain advantage in the perimeter security system.However,due to the complex and changeable external environment,there are various environmental interferences in the signals detected by the optical fiber sensor,resulting in many false positives and false negatives.Whether various event signals can be identified accurately and in real time has become the key technology of the optical fiber perimeter security system.Therefore,only by combining intelligent,advanced,mature and reliable scientific and technological means to establish a perimeter security system that combines signal monitoring,signal recognition,intrusion alarm,video confirmation,etc.can solve the problem of false alarms and false alarms in the security system.At present,most of the signal recognition adopts methods such as artificial extraction of features and labeling of categories,resulting in a high dependence of the classifier on the environment and professionals.How to improve the system’s ability to autonomously identify various events and reduce the dependence on professionals is one of the important issues that the optical fiber perimeter security system urgently needs to solve.In view of the above background,this paper studies the analysis technology of optical fiber vibration signal as follows:(1)A method of vibration signal recognition based on multiple features in time domain,frequency domain and wavelet domain is proposed.Research and compare the principles,advantages and disadvantages of existing feature extraction methods.Mining the characteristic information differences in the time domain,frequency domain and wavelet domain of the data collected in the laboratory environment.Four eigenvalues of short-term over-level rate and energy in time domain,frequency domain energy and wavelet packet energy are selected as the threshold conditions for signal pre-judgment.The experimental verification is completed,the signal that does not reach the judgment condition is input into the algorithm model to complete the identification work;the signal greater than the judgment condition is directly judged as an intrusion signal,which effectively improves the real-time performance of the system.And save this part of the intrusion signal to the database,enrich the training data samples of the algorithm model,and improve the accuracy of algorithm identification.(2)According to the outstanding advantages of LSTM in the field of natural language processing,an LSTM model that does not require manual extraction of feature values is proposed and implemented for this system,which reduces the impact of manual extraction of features on the accuracy of the model.Input the unfiltered signals in the time-frequency domain and wavelet domain into the LSTM model for identification and classification.The experimental results show that the accuracy is about 92%,which verifies the effectiveness of LSTM algorithm in the field of optical fiber perimeter security.(3)An optimization algorithm model based on LSTM is proposed.The LSTM algorithm model is improved and optimized from three aspects:optimizer,activation function and adding Attention Mechanism.It is concluded that the AM-LSTM algorithm is more effective and more accurate,and more suitable for the analysis of optical fiber vibration signals;the experimental verification has an accuracy rate of about 96%.(4)Support Vector Machines(SVM)algorithm,LSTM algorithm and AM-LSTM algorithm are proposed to identify and classify the same data set respectively.The experiment compares the recognition accuracy and performance of the three algorithms,and it is concluded that the accuracy of the AM-LSTM algorithm is better than the SVM algorithm and the LSTM algorithm.