OTDR Signal Processing System Using Wavelet Adaptive Filtering
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Solution Overview
Problem
Conventional OTDR systems face challenges in effectively processing non-stationary noise sources, such as instantaneous impacts and short-term temperature changes, which degrade the signal-to-noise ratio and affect measurement accuracy.
Innovation Solution
A signal processing system combining wavelet-based self-adaptive filtering and time piecewise measurement, utilizing a self-adaptive filter to reduce noise from different electrical amplifiers and discarding invalid data, improves the signal-to-noise ratio and supports accurate calculation of attenuation parameters and event distances.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If conventional filters (FIR, wavelet transform) are used to suppress stationary noise, then stationary noise suppression is improved, but non-stationary noise remains ineffective
Solution Approach 1:
The patent applies dynamics by making the filter adaptive rather than fixed. The filter coefficients are continuously updated based on the statistical characteristics of the input signal, allowing the system to automatically adjust to changing noise conditions. This enables effective suppression of both stationary and non-stationary noise, resolving the contradiction between the two.
Solution Approach 2:
The patent implements feedback mechanisms where the output of the filter is fed back to update the filter coefficients. The system continuously monitors the signal characteristics and adjusts the filtering parameters accordingly, creating a closed-loop control system that adapts to varying noise conditions in real-time.
2Measurement precision
If optical pulses are sent multiple times and arithmetic average is calculated to improve signal-to-noise ratio, then signal-to-noise ratio is improved, but measurement time increases from 5 seconds to 50 seconds
Solution Approach 1:
The patent changes the parameter of filtering approach from traditional fixed-filter methods to adaptive filtering with dynamically adjusted coefficients. This allows the system to achieve better noise suppression with fewer averaging operations, significantly reducing measurement time while maintaining or improving signal-to-noise ratio.
Solution Approach 2:
The patent replaces the mechanical approach of repeated physical measurements and averaging with an intelligent signal processing approach using adaptive filters. Instead of physically repeating the measurement multiple times, the system uses computational methods to achieve equivalent or superior noise suppression in a single pass or with minimal repetitions.
3Measurement precision
If high-sensitivity receiving circuit is used to obtain larger dynamic range for longer-distance optical fiber measurement, then dynamic range is improved, but vulnerability to noise interference increases
Solution Approach 1:
The patent applies preliminary action by pre-adapting the filter coefficients to match the statistical characteristics of the noise before the actual measurement takes place. The system performs initial characterization of the noise environment and configures the adaptive filter accordingly, so that when high-sensitivity reception is enabled, the filter is already optimized to suppress the specific noise patterns present in that environment.
Solution Approach 2:
The adaptive filter acts as an intermediary between the high-sensitivity receiving circuit and the signal processing chain. It selectively attenuates noise components while preserving the useful signal, allowing the high-sensitivity circuit to operate at full capability without being overwhelmed by noise interference.
Data Source
AI summary
The present disclosure relates to a signal processing system applied to remove OTDR noise, comprising: an analog-to-digital converter, a laser and driving unit, a sequence accumulator, a preprocessing counter, a pulse generator, a dual-port memory, a self-adaptive filter, an event decision device, and a preprocessing data decision device. The self-adaptive filter reads preprocessing data from a read-only port of the dual-port memory, and performs noise processing on the read preprocessing data by using a self-adaptive filtering method of wavelet transform. The event decision device performs an event decision on the filtered data output from the self-adaptive filter; The preprocessing data decision device decides whether a certain set of preprocessing data in m groups of preprocessing data is correct data or high-signal-to-noise data according to the difference of the m groups of preprocessing data after passing through the event decision device.