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Related Concept Videos

Aliasing01:18

Aliasing

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Accurate signal sampling and reconstruction are crucial in various signal-processing applications. A time-domain signal's spectrum can be revealed using its Fourier transform. When this signal is sampled at a specific frequency, it results in multiple scaled replicas of the original spectrum in the frequency domain. The spacing of these replicas is determined by the sampling frequency.
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Managing signal sampling rates is essential in digital signal processing to maintain signal integrity. A decimated signal, characterized by a reduced frequency range due to its lower sampling rate, can be upsampled by inserting zeros between each sample. This upsampling process expands the original spectrum and introduces repeated spectral replicas at intervals dictated by the new Nyquist frequency. To refine this zero-inserted sequence, it is passed through a lowpass filter with a cutoff...
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Bandpass Sampling01:17

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In signal processing, bandpass sampling is an effective technique for sampling signals that have most of their energy concentrated within a narrow frequency band. This type of signal is known as a bandpass signal. The key principle of bandpass sampling involves sampling the signal at a rate that is greater than twice the signal's bandwidth to prevent aliasing.
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Fiber Optic Distributed Sensors for High-resolution Temperature Field Mapping
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Adaptive Temporal Matched Filtering for Noise Suppression in Fiber Optic Distributed Acoustic Sensing.

İbrahim Ölçer1,2, Ahmet Öncü3

  • 1TÜBİTAK BİLGEM, Barış Mah., Dr. Zeki Acar Cad., Gebze 41470, Kocaeli, Turkey. ibrahim.olcer@tubitak.gov.tr.

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Summary

This study introduces a novel temporal adaptive processing method to reduce fading noise in fiber optic distributed acoustic vibration sensing. The technique enhances signal-to-noise ratio by over 10 dB without affecting system bandwidth.

Keywords:
Rayleigh scatteringWiener filtersadaptive temporal filteringfiber optic sensorsmatched filtersstructural health monitoringvibration detection

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Area of Science:

  • Optoelectronics
  • Signal Processing
  • Fiber Optic Sensing

Background:

  • Phase-sensitive optical time domain reflectometry (ϕ-OTDR) is crucial for distributed vibration sensing.
  • Fading noise in coherent detection-based ϕ-OTDR systems degrades performance.
  • Existing methods struggle with high-frequency events and impact system bandwidth.

Purpose of the Study:

  • To present a new digital processing technique for reducing fading noise in fiber optic distributed acoustic vibration sensing.
  • To improve the signal-to-noise ratio (SNR) without compromising the frequency response.
  • To offer a solution effective for high-frequency vibration detection.

Main Methods:

  • Temporal adaptive processing of ϕ-OTDR signals.
  • Algorithm based on signal-to-noise ratio (SNR) maximization.
  • Validation through laboratory experiments and field tests.

Main Results:

  • Achieved over 10 dB improvement in SNR values.
  • Demonstrated no reduction in system bandwidth.
  • Showcased the effectiveness without additional optical hardware.

Conclusions:

  • The proposed adaptive processing approach effectively mitigates fading noise in ϕ-OTDR systems.
  • This method enhances distributed acoustic vibration sensing performance without bandwidth limitations.
  • The technique is suitable for developing advanced fiber optic-based sensing systems.