This work presents a detailed review of the development of distributed acoustic sensors (DAS) and their newest scientific applications. It covers most areas of human activities, such as the engineering, material, and humanitarian sciences, geophysics, culture, biology, and applied mechanics. It also provides the theoretical basis for most well-known DAS techniques and unveils the features that characterize each particular group of applications. After providing a summary of research achievements, the paper develops an initial perspective of the future work and determines the most promising DAS technologies that should be improved.

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We propose a novel approach to the recognition of particular classes of non-conventional events in signals from phase-sensitive optical time-domain-reflectometry-based sensors. Our algorithmic solution has two main features: filtering aimed at the de-nosing of signals and a Gaussian mixture model to cluster them. We test the proposed algorithm using experimentally measured signals. The results show that two classes of events can be distinguished with the best-case recognition probability close to 0.9 at sufficient numbers of training samples.The problem of the development of novel sensor techniques plays a crucial role in science and technology. One of the most important classes of sensing systems is distributed sensors, which are of great importance for the remote control of extended objects [1][2][3][4][5][6][7][8]. Phase-sensitive coherent optical time-domain reflectometry (Φ-OTDR) is a basic technique that can provide sufficient sensitivity and resolution for these distributed sensing systems [4][5][6]. Standard OTDR techniques use light sources with coherence lengths, which are shorter than pulse lengths. This can yield a sum of backscattered intensities from each scattering center, which allows one, e.g., to control splices and breaks in fiber cables [8]. On the contrary, in Φ-OTDR-based sensing systems [9][10][11][12][13][14], the coherence length of lasers is longer than their pulse length. An event near the optical fiber generates an acoustic wave that affects the fiber by changing the phases of the backscattering centers. An analysis of such signals can reveal their impact on the sensor and monitor located near fiber objects [9][10][11][12][13][14]. A key stage in implementing Φ-OTDRbased sensors is the development of an algorithmic solution to reveal unusual vibrations in the background.The problem of the recognition of non-conventional activity (a target event) consists of two closely related subproblems. The first is related to the de-noising procedure, which allows the detection of an event in the background with high probability. The second and much more important subproblem is the development of a classification methods aimed at clustering detected target events into predetermined classes. Due to the complex structure of the signals in such sensors, this is challenging [15][16][17][18][19][20][21][22][23].In addition to guaranteeing high accuracy of recognition, post-processing algorithms for Φ-OTDR-based sensors should be able to operate rapidly without significant computational resources. In other cases, their application in real-time distributed fiber optic sensing systems is substantially limited. In vibration sensing systems based on Φ-OTDR-based, there is at present no sufficiently fast and versatile algorithmic solution for the recognition of events. A promising direction for the solution of this problem is using of a machine learning toolbox, in particular neural networks and pattern recognition techniques [20][21][22][23]. Recent results [21][22][23] have shown that recognition algorithm...

In this study, an experimental study of the burning rate of solid fuel in a model solid propellant rocket motor (SRM) E-5-0 was conducted using a non-invasive control method with fiber-optic sensors (FOSs). Three sensors based on the Mach–Zehnder interferometer (MZI), fixed on the SRM E-5-0, recorded the vibration signal during the entire cycle of solid fuel burning. The results showed that, when using MZI sensors, the non-invasive control of solid fuel burnout is made possible both by recording the time of arrival of the combustion front to the sensor and by analyzing the peaks on the spectrogram of the recorded FOS signal. The main mode of acoustic vibrations of the chamber of the model SRM is longitudinal, and it changes with time, depending on the chamber length. Longitudinal modes of the combustion chamber were detected by MZI only after the combustion front passed its fixing point, and the microphone was unable to register them at all. The results showed that the combustion rate was practically constant after the first second, which was confirmed by the graph of the pressure versus time at the nozzle exit.