Open Access
Distributed vibration sensing based on optical vector network analysis
(IEEE, 2024-10-28) Loayssa Lara, Alayn; Vallifuoco, Raffaele; Zahoor, Rizwan; Zeni, Luigi; Sagüés García, Mikel; Minardo, Aldo; Ingeniería Eléctrica, Electrónica y de Comunicación; Ingeniaritza Elektrikoa, Elektronikoa eta Telekomunikazio Ingeniaritza; Institute of Smart Cities - ISC; Universidad Pública de Navarra / Nafarroako Unibertsitate Publikoa
We introduce a novel method for distributed vibration sensing based on extracting the time-domain Rayleigh impulse response of an optical fiber from optical vector network analysis measurements. The optical-frequency-domain transfer function of the fiber is first measured, and then inverse Fourier transformed to provide the bandpass optical time-domain impulse response. Another relevant feature of the technique is that it enables excitation demodulation using the optical frequency dependence of the Rayleigh backscatter signal from the optical fiber, the so-called Rayleigh signature. This is the simplest method to obtain fully linear quantitative measurements of local changes in the strain or temperature experienced by the fiber and it is inherently free from signal fading impairments. Furthermore, the implementation of the technique uses a simple setup based on double-sideband modulation of a laser, self-homodyne detection with an optical hybrid, and narrow-bandwidth electrical signal acquisition and processing. We present proof-of-concept experiments to demonstrate the operation of the method with the measurement of dynamic strain and temperature perturbations in a 115-m optical sensing fiber with 16-cm spatial resolution and a sensitivity of 59 nHz. This sensing technique has the potential to provide high-sensitivity distributed measurements of tens-of-hertz excitations in hundreds-of-meters fibers, with centimeter spatial resolution. Therefore, it can become a valuable tool for structural health monitoring in application fields such as aerospace, marine, or civil engineering.
Open Access
Method to use transport microsimulation models to create synthetic distributed acoustic sensing datasets
(MDPI, 2025-05-07) Robles Urquijo, Ignacio; Benavente, Juan; Blanco García, Javier; Diego González, Pelayo; Loayssa Lara, Alayn; Sagüés García, Mikel; Rodríguez Cobo, Luis; Cobo, Adolfo; Ingeniería Eléctrica, Electrónica y de Comunicación; Ingeniaritza Elektrikoa, Elektronikoa eta Telekomunikazio Ingeniaritza; Institute of Smart Cities - ISC
This research introduces a new method for creating synthetic Distributed Acoustic Sensing (DAS) datasets from transport microsimulation models. The process involves modeling detailed vehicle interactions, trajectories, and characteristics from the PTV VISSIM transport microsimulation tool. It then applies the Flamant-Boussinesq approximation to simulate the resulting ground deformation detected by virtual fiber-optic cables. These synthetic DAS signals serve as large-scale, scenario-controlled, labeled datasets on training machine learning models for various transport applications. We demonstrate this by training several U-Net convolutional neural networks to enhance spatial resolution (reducing it to half the original gauge length), filtering traffic signals by vehicle direction, and simulating the effects of alternative cable layouts. The methodology is tested using simulations of real road scenarios, featuring a fiber-optic cable buried along the westbound shoulder with sections deviating from the roadside. The U-Net models, trained solely on synthetic data, showed promising performance (e.g., validation MSE down to 0.0015 for directional filtering) and improved the detectability of faint signals, like bicycles among heavy vehicles, when applied to real DAS measurements from the test site. This framework uniquely integrates detailed traffic modeling with DAS physics, providing a novel tool to develop and evaluate DAS signal processing techniques, optimize cable layout deployments, and advance DAS applications in complex transportation monitoring scenarios. Creating such a procedure offers significant potential for advancing the application of DAS in transportation monitoring and smart city initiatives.
Open Access
Compensation of phase noise impairments in distributed acoustic sensors based on optical pulse compression time-domain reflectometry
(IEEE, 2023) Piñeiro Ben, Enrique; Sagüés García, Mikel; Loayssa Lara, Alayn; Ingeniería Eléctrica, Electrónica y de Comunicación; Institute of Smart Cities - ISC; Ingeniaritza Elektrikoa, Elektronikoaren eta Telekomunikazio Ingeniaritzaren
We introduce a method to compensate for the deleterious effects of the phase noise of the laser source on long-range distributed acoustic sensors (DAS) that implement optical pulse compression (OPC). Pulse compression can be used in coherent optical time-domain reflectometry (COTDR) sensors to extend the measurement range without compromising spatial resolution. In fact, OPC-COTDR sensors have demonstrated the longest measurement range to date in passive sensing links that do not require distributed amplification to compensate fiber attenuation. However, it has been found that the limited coherence of the laser source has a degrading effect on the actual performance enhancement that pulse compression can bring because it constrains the maximum duration of the compression waveforms that can be used and makes the use of lasers with extremely low phase noise necessary.We introduce a technique to compensate for the effects of phase noise on OPC-COTDR sensors so that they can demonstrate their full potential for long-range measurements using lasers with less stringent phase noise requirements. The method is based on sampling the phase noise of the laser with an auxiliary interferometer and using this information in a simple signal processing technique to mitigate its deleterious effect on the signal measured. We test our method in an OPCCOTDR sensor that uses 500-μs linear frequency modulated pulses to demonstrate 100-km range measurements with 200 p/√Hz of strain sensitivity at 2-m initial spatial resolution that becomes 10-m after applying the gauge length. To our knowledge, this is the longest compression waveform demonstrated to date in an OPCCOTDR sensor. Its use provides an extra 20-km range compared to previous demonstrations using laser sources of comparable linewidth. Furthermore, comparable performance is also demonstrated when using a laser source with an order of magnitude larger linewidth.
Open Access
Long-range traffic monitoring based on pulse-compression distributed acoustic sensing and advanced vehicle tracking and classification algorithm
(MDPI, 2023) Corera Orzanco, Íñigo; Piñeiro Ben, Enrique; Navallas Irujo, Javier; Sagüés García, Mikel; Loayssa Lara, Alayn; Ingeniería Eléctrica, Electrónica y de Comunicación; Institute of Smart Cities - ISC; Ingeniaritza Elektrikoa, Elektronikoaren eta Telekomunikazio Ingeniaritzaren
We introduce a novel long-range traffic monitoring system for vehicle detection, tracking, and classification based on fiber-optic distributed acoustic sensing (DAS). High resolution and long range are provided by the use of an optimized setup incorporating pulse compression, which, to our knowledge, is the first time that is applied to a traffic-monitoring DAS system. The raw data acquired with this sensor feeds an automatic vehicle detection and tracking algorithm based on a novel transformed domain that can be regarded as an evolution of the Hough Transform operating with non-binary valued signals. The detection of vehicles is performed by calculating the local maxima in the transformed domain for a given time-distance processing block of the detected signal. Then, an automatic tracking algorithm, which relies on a moving window paradigm, identifies the trajectory of the vehicle. Hence, the output of the tracking stage is a set of trajectories, each of which can be regarded as a vehicle passing event from which a vehicle signature can be extracted. This signature is unique for each vehicle, allowing us to implement a machine-learning algorithm for vehicle classification purposes. The system has been experimentally tested by performing measurements using dark fiber in a telecommunication fiber cable running in a buried conduit along 40 km of a road open to traffic. Excellent results were obtained, with a general classification rate of 97.7% for detecting vehicle passing events and 99.6% and 85.7% for specific car and truck passing events, respectively.

Sagüés García, Mikel

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