Deep learning algorithms for signal recognition in long perimeter monitoring distributed fiber optic sensors

Abstract

Abstract. In this paper, we show an approach to build deep learning algorithms for recognizing signals in distributed fiber optic monitoring and security systems for long perimeters. Synthesizing such detection algorithms poses a non-trivial research and development challenge, because these systems face stringent error (type I and II) requirements and operate in difficult signal-jamming environments, with intensive signal-like jamming and a variety of changing possible signal portraits of possible recognized events. To address these issues, we have developed a two-level event detection architecture, where the primary classifier is based on an ensemble of deep convolutional networks, can recognize 7 classes of signals and receives time-space data frames as input. Using real-life data, we have shown that the applied methods result in efficient and robust multiclass detection algorithms that have a high degree of adaptability.

Artificial Intelligence Approach for Target Classification: A State of The Art

“…Machine Learning is a sub-branch of artificial intelligence, which consists of creating algorithms capable of improving automatically with experience [45][46][47][48][49][50]. We also speak in this case of self-learning systems.…”

“…Artificial intelligence (AI) has come to the fore in recent years. It is used in several applications for various disciplines [41][42][43][44][45][46][47][48][49][50][51][52][53][54]. Artificial Intelligence (AI) as we know it is weak Artificial Intelligence, as opposed to strong AI, which does not exist yet.…”

Fibre-optic based particle sensing via deep learning

“…In the field of optics, deep learning has led to classification and predictive capability in laser ablation [36][37][38], advances in microscopy [39,40], label-free cell classification [41], object classification through scattering media [42][43][44][45] and through scattering pattern imaging of plastic microparticles, cells, spores and colloidal particles [46][47][48][49][50][51]. In the field of fibre-optics, deep learning is gaining interest [52], with work having been reported for perimeter monitoring [53], self-tuning mode-locked fibre lasers [54], and for fibre-optics being used to classify and reconstruct the input handwritten digits and photographs from the speckle patterns transmitted through multimode fibre [55][56][57]. In addition, deep learning has been used in optical communications [58,59], and more specifically, for real-time fibre mode demodulation [60], end-to-end fibre communications [61], and improvement in fibre transmission [62].…”

Fractal Complexity-Based Feature Extraction Algorithm of Communication Signals

“…offering command, control, and intelligence. Nowadays, as electronic information technology develops more and more rapidly, and the modulation pattern of communication signal becomes more and more complex, the number of communication radiation source individual equipment also increases gradually, which makes the information acquisition of communication radiation source more and more difficult, [1][2][3] and then brings great challenges for communication detection in the electronic confrontation field. In such a complex electromagnetic environment, it is difficult to meet the needs of modern battlefield by traditional means of communication detection, and using simple feature parameters, [4][5][6] such as modulation parameters, carrier frequency, bandwidth, symbol rate, electric level, cannot achieve the purpose of identification of individual radiation sources.…”