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Runways monitoring in civil airports is typically performed by enhanced radar surveillance systems operating in the so-called Precision Runway Monitor (PRM) system that provides position of airplanes and information on their trajectories. In this paper, we propose an additional measurement system that can be used jointly with the conventional PRM approach to monitor aircrafts position on land without using the radar approach or requiring visual inspection of the lanes. We propose a system made of ground-based distributed optical fiber sensors (OFSs), to be placed below the concrete floor of the take-off and landing lanes as well as along the different roads and intersections within the airport. OFSs will be read by a phase-sensitive OTDR (Φ-OTDR) driven by low-noise narrow-linewidth 1.55 mm laser. The laser field is used to inject the optical fiber with a pulsed signal and to read the phase shifts arising from vibrations and pressure waves produced by aircrafts moving, or even still with engines turned on, along the lanes. An absolute localization of different planes, with a few meters resolution and accuracy across the whole airport, would be possible after installing OFSs and adjustment of the detection system. The proposed ground monitoring system, in addition to the ones currently available, offers information redundancy at a promising low-cost and a backup aircraft-positioning tool in case of failure or malfunctioning of the existing video-or radio-based systems. Furthermore, the proposed system is inherently immune to e.m. interference and also to intentional disturbance or intrusion. Design of the laser source and of the Φ-OTDR measurement system is underway with the aim of precise and continuous monitoring of the on-land position and movement of different aircrafts or vehicles within the airport

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...

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The present work studies the influence of laser frequency drifts on operating of phase-sensitive optical time-domain reflectometry (Φ-OTDR) fiber sensors. A mathematical model and numerical simulations are employed to highlight the influence of frequency drifts of light sources on two characteristic scales: large-time (minutes) and short-time (milliseconds) frequency drifts. Numerical simulation results are compared with predictions given by the fluctuation ratio coefficient (FRC), and they are in a qualitative agreement. In addition to qualitative criteria for light sources given by the FRC, quantitive requirements for optimal light sources for Φ-OTDR sensors are obtained. Numerical simulation results are verified by comparison with experimental data for three significantly different types of light source.