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Probability of signal demodulation jump errors in the maximum-likelihood-estimation algorithm for a low SNR

Yi Qi, Peng Zhang, Yuru Chen

|March 10, 2021

Summary

Jump errors in optical fiber sensors using maximum-likelihood estimation (MLE) can be predicted. This study introduces a quantitative method to calculate jump error probability based on signal-to-noise ratio (SNR), identifying an essential SNR threshold.

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Last Updated: Nov 14, 2025

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Quasi-light Storage for Optical Data Packets

Quasi-light Storage for Optical Data Packets

Published on: February 6, 2014

Area of Science:

Optical Fiber Sensing
Signal Processing
Metrology

Background:

Maximum-likelihood estimation (MLE) is crucial for optical fiber sensor data analysis.
Low signal-to-noise ratio (SNR) in optical fiber sensors can lead to significant errors.
Jump errors in MLE algorithms degrade sensor accuracy and reliability.

Purpose of the Study:

To develop a quantitative method for predicting jump errors in MLE algorithms.
To establish a signal-to-noise ratio (SNR) threshold for mitigating jump errors.
To analyze the relationship between SNR and the probability of jump errors.

Main Methods:

Quantitative calculation method based on the maximum-likelihood estimation (MLE) algorithm.
Simulation of jump error probability across a range of signal-to-noise ratios (SNRs).
Experimental verification of simulation findings for low-finesse interference spectra.

Main Results:

A method to quantitatively calculate the probability of jump errors as a function of SNR.
Determination of an SNR threshold crucial for the MLE algorithm in low-finesse interference spectra.
Exponential decrease in jump error probability with increasing SNR from 0 dB to 25 dB.

Conclusions:

The proposed quantitative method accurately predicts jump errors in optical fiber sensors.
Experimental results confirm the simulation's findings on the SNR-dependent nature of jump errors.
Establishing an appropriate SNR threshold is vital for reliable MLE-based optical fiber sensing.