Compressive Sampling Filter for Distributed Fiber Optic Sensing
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Solution Overview
Problem
Conventional distributed fiber optic sensing systems face challenges in achieving high spatial resolution due to the high data storage and processing requirements, which degrade the performance of optical receivers and analog-to-digital converters as bandwidth increases, leading to compromised integrity of sensed parameters.
Innovation Solution
The implementation of compressive sensing techniques using a compressive sampling filter to selectively block or combine backscattered light from fiber optic sensors, reducing the need for high-bandwidth components and enabling lower speed and bandwidth systems while maintaining high spatial resolution, by applying pseudo-random spatial filtering and signal processing methods.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the optical receiver detects and samples the backscattered signal at high speed to achieve desired spatial resolution, then spatial resolution is improved, but the performance of the receiver is degraded due to increased bandwidth requirements
Solution Approach 1:
The patent applies compressive sampling techniques that perform preliminary processing of the backscattered signal by selectively combining samples from different spatial locations before detection. This preliminary action reduces the dimensionality of the signal, allowing high spatial resolution to be achieved without requiring high-speed detection, thereby maintaining receiver performance.
Solution Approach 2:
The patent transforms the sampling problem from the time domain to a compressed domain by changing the sampling parameters. Instead of sampling at high rates corresponding to fine spatial intervals, the system uses lower sampling rates with compressive sampling matrices that reconstruct high-resolution spatial information, thus avoiding bandwidth-related performance degradation.
2Measurement precision
If the bandwidth of the optical receiver is increased to achieve high spatial resolution, then spatial resolution is improved, but the performance of the receiver is degraded by a proportional amount
Solution Approach 1:
Compressive sampling performs preliminary dimensionality reduction on the backscattered signal, combining information from multiple spatial locations into fewer measurement channels. This allows the system to achieve high spatial resolution with lower bandwidth receivers, preventing the performance degradation that would otherwise occur with high bandwidth requirements.
3Measurement precision
If high speed and bandwidth system components are used to sample the backscattered signal at high speed, then spatial resolution is improved, but data storage and processing requirements increase
Solution Approach 1:
The patent fundamentally changes the sampling parameters by using compressive sampling matrices that project high-dimensional spatial information into lower-dimensional measurement space. This parameter transformation reduces the number of samples that need to be stored and processed while preserving the ability to reconstruct high-resolution spatial distributions through appropriate algorithms.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enhances the performance of distributed fiber optic sensors by reducing data storage and bandwidth requirements, achieving higher resolution without increasing sampling rates, and providing lower-cost, lower-power components while maintaining or improving signal fidelity and noise reduction.
Implementation Method 1
The backscatter signal of interest may consist of one or a combination of Rayleigh, Brillouin, or Raman backscatter
Implementation Method 2
The backscatter signal of interest may consist of one or a combination of Rayleigh, Brillouin, or Raman backscatter
Implementation Method 3
The backscatter signal of interest may consist of one or a combination of Rayleigh, Brillouin, or Raman backscatter
Data Source
AI summary
Distributed optical sensing systems utilize compressive sensing techniques to determine parameters sensed by a waveguide. The system generates light that is sent along a sensing waveguide, thereby producing backscattered light. A compressive sampling filter forms part of the system, and is used to selectively block portions of the generated light or the backscattered light. The backscattered light is received by a receiver and used to determine one or more parameters.