Jian Fang NEC Labs America

Jian Fang

Researcher

Optical Networking & Sensing

Posts

Drone Detection and Localization using Enhanced Fiber-Optic Acoustic Sensor and Distributed Acoustic Sensing Technology

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Drone Detection and Localization using Enhanced Fiber-Optic Acoustic Sensor and Distributed Acoustic Sensing Technology In recent years, the widespread use of drones has led to serious concerns about safety and privacy. Drone detection using microphone arrays has proven to be a promising method. However, it is challenging for microphones to serve large-scale applications due to the issues of synchronization, complexity, and data management. Moreover, distributed acoustic sensing (DAS) using optical fibers has demonstrated its advantages in monitoring vibrations over long distances but does not have the necessary sensitivity for weak airborne acoustics. In this work, we present, to the best of our knowledge, the first fiber-optic quasi-distributed acoustic sensing demonstration for drone surveillance. We develop enhanced fiber-optic acoustic sensors (FOASs) for DAS to detect drone sound. The FOAS shows an ultra-high measured sensitivity of −101.21 re. 1rad/µPa, as well as the capability for high-fidelity speech recovery. A single DAS can interrogate a series of FOASs over a long distance via optical fiber, enabling intrinsic synchronization and centralized signal processing.We demonstrate the field test of drone detection and localization by concatenating four FOASs as DAS. Both the waveforms and spectral features of the drone sound are recognized. With acoustic field mapping and data fusion, accurate drone localization is achieved with a root-mean-square error (RMSE) of 1.47 degrees. This approach holds great potential in large-scale sound detection applications, such as drone detection or city event monitoring.

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Using Global Fiber Networks for Environmental Sensing

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Using Global Fiber Networks for Environmental Sensing We review recent advances in distributed fiber optic sensing (DFOS) and their applications. The scattering mechanisms in glass, which are exploited for reflectometry-based DFOS, are Rayleigh, Brillouin, and Raman scatterings. These are sensitive to either strain and/or temperature, allowing optical fiber cables to monitor their ambient environment in addition to their conventional role as a medium for telecommunications. Recently, DFOS leveraged technologies developed for telecommunications, such as coherent detection, digital signal processing, coding, and spatial/frequency diversity, to achieve improved performance in terms of measurand resolution, reach, spatial resolution, and bandwidth. We review the theory and architecture of commonly used DFOS methods. We provide recent experimental and field trial results where DFOS was used in wide-ranging applications, such as geohazard monitoring, seismic monitoring, traffic monitoring, and infrastructure health monitoring. Events of interest often have unique signatures either in the spatial, temporal, frequency, or wavenumber domains. Based on the temperature and strain raw data obtained from DFOS, downstream postprocessing allows the detection, classification, and localization of events. Combining DFOS with machine learning methods, it is possible to realize complete sensor systems that are compact, low cost, and can operate in harsh environments and difficult-to-access locations, facilitating increased public safety and smarter cities.

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Distributed Optical Fiber Sensing Using Specialty Optical Fibers

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Distributed Optical Fiber Sensing Using Specialty Optical Fibers Distributed fiber optic sensing systems use long section of optical fiber as the sensing media. Therefore, the fiber characteristics determines the sensing capability and performance. In this presentation, various types of specialty optical fibers and their sensing applications will be introduced and discussed.

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Field Trials of Vibration Detection, Localization and Classification over Deployed Telecom Fiber Cables

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Field Trials of Vibration Detection, Localization and Classification over Deployed Telecom Fiber Cables We review sensing fusion results of integrating fiber sensing with video for machine-learning-based localization and classification of impulsive acoustic event detection. Classification accuracy >97% was achieved on aerial coils, and >99% using fiber-based signal enhancers.

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Evolution of Fiber Infrastructure – From Data Transmission to Network Sensing

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Evolution of Fiber Infrastructure – From Data Transmission to Network Sensing We review multiple use cases over deployed networks including co-existing sensing/data transmission, cable cut prevention and perimeter intrusion detection to realize telecom infrastructure can be sensing backbones instead of the sole function of data transmission.

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Field Tests of Impulsive Acoustic Event Detection, Localization, and Classification Over Telecom Fiber Networks

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Field Tests of Impulsive Acoustic Event Detection, Localization, and Classification Over Telecom Fiber Networks We report distributed-fiber-optic-sensing results on impulsive acoustic events localization/classification over telecom networks. A deep-learning-based model was trained to classify starter-gun and fireworks signatures with high accuracy of > 99% using fiber-based-signal-enhancer and >97% using aerial coils.

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Template Matching Method with Distributed Acoustic Sensing Data and Simulation Data

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Template Matching Method with Distributed Acoustic Sensing Data and Simulation Data We propose a new method to detect acoustic signals by matching distributed acoustic sensing data with simulation. In the simulation of the dynamic strain on an optical fiber, the optical fiber layouts and the gauge length are properly incorporated. We apply the proposed method to the acoustic-source localization and demonstrate the method localizes the source accurately even under the layouts which include the straight optical fiber for the sensing points with the large gauge-length settings.

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Perimeter Intrusion Detection with Rayleigh Enhanced Fiber Using Telecom Cables as Sensing Backhaul

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Perimeter Intrusion Detection with Rayleigh Enhanced Fiber Using Telecom Cables as Sensing Backhaul We report field test results of facility perimeter intrusion detection with distributed-fiber-sensing technology and backscattering-enhanced-fiber by using deployed telecom fiber cables as sensing backhaul. Various intrusive activities, such as walking/jumping at >100ft distance, are detected.

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Remote Drone Detection and Localization with Optical Fiber Microphones and Distributed Acoustic Sensing

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Remote Drone Detection and Localization with Optical Fiber Microphones and Distributed Acoustic Sensing We demonstrate the first fiber-optic drone detection method with ultra-highly sensitive optical microphones and distributed acoustic sensor. Accurate drone localization has been achieved through acoustic field mapping and data fusion.

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Distributed Fiber Sensor Network Using Telecom Cables as Sensing Media: Technology Advancements and Applications

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Distributed Fiber Sensor Network Using Telecom Cables as Sensing Media: Technology Advancements and Applications Distributed fiber optic sensing (DFOS) is a rapidly evolving field that allows the existing optical fiber infrastructure for telecommunications to be reused for wide-area sensing. Using the backscattering mechanisms of glass—which includes Rayleigh, Brillouin, and Raman backscatter—it is possible to realize distributed vibration and temperature sensors with good sensitivity at every fiber position, and spatial resolution is determined by the bandwidth of the interrogation signal. In this paper, we will review the main technologies in currently deployed DFOS. We review the digital signal processing operations that are performed to extract the sensing parameters of interest. We report recent distributed vibration sensing, distributed acoustic sensing, and distributed temperature sensing field trial results over an existing network with reconfigurable add/drop multiplexers carrying live telecom traffic, showing that the network is capable of simultaneous traffic and temperature monitoring. We report Brillouin optical time-domain reflectometry experimental results for monitoring static strain on aerial fiber cables suspended on utility poles. Finally, we demonstrate an example of network modification to make passive optical networks compatible with DFOS by adding reflective semiconductor optical amplifiers at optical network units.

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