Distributed fiber optic sensors (DFOSs) and distributed acoustic sensors (DASs) can detect and classify signal patterns using advanced interferometric setups and deep learning algorithms for high-accuracy pattern recognition.

Overview of Fiber Optic Sensors

Distributed fiber optic sensors (DFOSs) and distributed acoustic sensors (DASs) are capable of monitoring long distances with high spatial resolution by detecting changes in light signals caused by vibrations, strain, or acoustic events along the fiber. DFOSs are widely used for intrusion detection, perimeter security, and structural monitoring, while DASs excel in detecting faint vibrations and acoustic signatures over kilometers of fiber .

Pattern Recognition Techniques

Interferometric Approaches

A Mach–Zehnder interferometer (MZI) can be combined with a time forest neural network (TFNN) to analyze signal patterns from DFOSs. This approach improves accuracy and efficiency compared to conventional one-dimensional convolutional neural networks (1D-CNNs), achieving up to 8.43% higher accuracy in intrusion detection scenarios . The interferometric method enhances sensor characterization and allows real-time signal processing in smart environments.

Deep Learning and CNNs

For DASs, deep convolutional neural networks (CNNs) are effective for classifying vibration events. Techniques such as intensity and phase stacked CNNs (IP-CNNs) utilize both intensity and phase information from coherent detection, improving classification accuracy. Data augmentation can further enlarge the training dataset, achieving classification accuracies of 88.2% for 1 km sensing lengths . These methods allow the system to distinguish between events like walking, digging, or vehicle movement.

Feature Extraction and Machine Learning

Other approaches include support vector machines (SVMs), relevance vector machines (RVMs), and artificial neural networks (ANNs) using features extracted from signal morphology, wavelet decomposition, or temporal-spatial data. These methods reduce false alarms and improve computational efficiency by focusing on relevant signal characteristics .

Applications

• Security and Intrusion Detection: Monitoring perimeters of military bases, industrial plants, and critical infrastructure .
• Structural Health Monitoring: Detecting strain, vibrations, or acoustic anomalies in pipelines, bridges, and buildings .
• Smart Environments: Real-time monitoring and classification of events for automated response systems .

Key Advantages

• High spatial resolution over long distances
• Real-time pattern recognition using advanced neural networks
• Ability to integrate both intensity and phase information for improved accuracy
• Flexibility to detect multiple types of events simultaneously Fiber optic sensors combined with modern machine learning and deep learning algorithms provide a powerful platform for accurate, real-time pattern recognition in diverse applications, from security to infrastructure monitoring .