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arxiv: 2604.23363 · v1 · submitted 2026-04-25 · ⚛️ physics.optics

All-Optical High-Resolution Real-Time Temperature Estimation Method Based on Fiber-Optic Interferometry

Jingwen Yang , Long Chen , Haoliang Yu , Xiaofeng Jin , Jianxiang Miao , Jia Kong This is my paper

Pith reviewed 2026-05-08 07:20 UTC · model grok-4.3

classification ⚛️ physics.optics
keywords fiber-optic interferometryextended Kalman filtertemperature estimationreal-time sensinghigh-resolutionnonlinear responseall-optical sensorelectromagnetic immunity
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The pith

An extended Kalman filter processes fiber-optic interferometer signals to estimate temperature in real time with 8.34e-5 K resolution under strong disturbances.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper develops an extended Kalman filter approach that models the nonlinear temperature-to-intensity relationship and the noise statistics of a fiber-optic interferometer. This modeling allows the filter to resolve phase ambiguity and reject environmental disturbances that defeat simpler inversion methods. Numerical tests reduce estimation error to 2.21e-5 K; laboratory experiments reach 8.34e-5 K resolution, a threefold gain over intensity-based inversion and an order-of-magnitude gain over conventional intensity measurement. The result supplies a compact, all-optical route to high-resolution temperature data that remains immune to electromagnetic interference.

Core claim

We develop an extended Kalman filter (EKF)-based approach that incorporates system non-linearity and noise statistics to enable robust real-time temperature estimation from interferometric signals. In numerical simulations, our EKF-based method reduces the estimation error to 2.21e-5 K, while experiments achieve a resolution of 8.34e-5 K under strong disturbances, corresponding to a threefold improvement over conventional intensity-based inversion method and an order-of-magnitude enhancement compared with traditional based measurement.

What carries the argument

The extended Kalman filter that recursively fuses the measured intensity signal with a nonlinear model of the temperature-intensity curve and the known noise statistics of the interferometer.

Load-bearing premise

The nonlinear temperature-intensity curve and the noise statistics of the actual interferometer must match the mathematical model used inside the filter.

What would settle it

A laboratory test in which the real interferometer exhibits temperature-to-intensity behavior or noise statistics outside the range assumed by the filter, producing no resolution improvement over simple intensity inversion.

Figures

Figures reproduced from arXiv: 2604.23363 by Haoliang Yu, Jia Kong, Jianxiang Miao, Jingwen Yang, Long Chen, Xiaofeng Jin.

Figure 2
Figure 2. Figure 2: From non-steady to steady-state simulation and view at source ↗
Figure 1
Figure 1. Figure 1: Simulation of a dynamic heating process. The view at source ↗
Figure 3
Figure 3. Figure 3: Schematic of the experimental setup. A distributed Bragg reflector (DBR) laser operating at 795 nm is used as view at source ↗
Figure 4
Figure 4. Figure 4: Heating process experimental results. (a) Mea view at source ↗
read the original abstract

High-resolution temperature monitoring is essential for many engineering and scientific applications, but conventional sensors are limited by insufficient resolution and susceptibility to electromagnetic interference. Fiber-optic interferometers provide high sensitivity and intrinsic electromagnetic immunity; however, their practical performance is hindered by nonlinear temperature-intensity responses, phase ambiguity, and environmental disturbances. Here, we develop an extended Kalman filter (EKF)-based approach that incorporates system non-linearity and noise statistics to enable robust real-time temperature estimation from interferometric signals. In numerical simulations, our EKF-based method reduces the estimation error to 2.21e-5 K, while experiments achieve a resolution of 8.34e-5 K under strong disturbances, corresponding to a threefold improvement over conventional intensity-based inversion method and an order-of-magnitude enhancement compared with traditional based measurement. These results demonstrate a compact and robust strategy for high-resolution, real-time, all-optical temperature sensing with strong immunity to electromagnetic interference.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The manuscript proposes an extended Kalman filter (EKF) method for real-time temperature estimation from fiber-optic interferometric intensity signals. It incorporates the nonlinear temperature-intensity response and noise statistics to address phase ambiguity and disturbances. Numerical simulations report an estimation error of 2.21e-5 K; experiments under strong disturbances achieve 8.34e-5 K resolution, claimed as a threefold improvement over conventional intensity-based inversion and an order-of-magnitude gain over traditional methods.

Significance. If the noise-model assumptions hold and the reported gains are reproducible, the approach would offer a compact, all-optical, EMI-immune sensor with real-time high-resolution performance suitable for engineering and scientific monitoring applications.

major comments (2)
  1. [EKF-based estimation method (likely §3)] The EKF formulation relies on process and measurement noise covariance matrices (Q and R) as free parameters whose values determine the filter's error statistics. The manuscript does not state how these matrices were obtained (separate calibration runs versus the same data set) or include a sensitivity study showing robustness to mismatch between assumed and actual disturbance statistics.
  2. [Experimental validation and results] The experimental claim of 8.34e-5 K resolution under strong disturbances and the threefold improvement over intensity-based inversion rest on the assumption that real disturbances are adequately captured by the Gaussian noise model inside the EKF. No explicit comparison of residual statistics or unmodeled components (e.g., polarization drift, non-Gaussian phase noise) is provided to support this attribution.
minor comments (2)
  1. [Theory and methods] Notation for the interferometric intensity-to-phase mapping and the EKF state vector should be defined consistently between the theoretical derivation and the numerical/experimental sections.
  2. [Figures] Figure captions for the simulation and experimental time-series plots should include the exact disturbance amplitudes and the conventional-method baseline curves for direct visual comparison.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback on our manuscript describing the EKF-based temperature estimation method from fiber-optic interferometry. We address each major comment below with clarifications and commitments to revisions that strengthen the presentation without altering the core results.

read point-by-point responses

  1. Referee: [EKF-based estimation method (likely §3)] The EKF formulation relies on process and measurement noise covariance matrices (Q and R) as free parameters whose values determine the filter's error statistics. The manuscript does not state how these matrices were obtained (separate calibration runs versus the same data set) or include a sensitivity study showing robustness to mismatch between assumed and actual disturbance statistics.

    Authors: We appreciate the referee's emphasis on this implementation detail. The covariance matrices Q and R were determined from independent calibration experiments conducted prior to the main trials, using controlled temperature steps and measured noise characteristics under quiescent conditions. In the revised manuscript we will explicitly describe this calibration procedure. We will also add a sensitivity analysis in which Q and R are varied by factors of 0.5 and 2 around the nominal values; the resulting estimation errors remain below 3.0e-5 K in simulation, confirming that performance is robust to moderate mismatches in the assumed noise statistics. revision: yes

  2. Referee: [Experimental validation and results] The experimental claim of 8.34e-5 K resolution under strong disturbances and the threefold improvement over intensity-based inversion rest on the assumption that real disturbances are adequately captured by the Gaussian noise model inside the EKF. No explicit comparison of residual statistics or unmodeled components (e.g., polarization drift, non-Gaussian phase noise) is provided to support this attribution.

    Authors: We agree that additional evidence linking the observed improvement to the Gaussian noise model would be valuable. The disturbances applied in the experiment were generated to produce statistics consistent with the model used in the EKF, and the reported resolution is obtained directly from the variance of the filtered temperature estimates. In revision we will include time-series plots of the measurement residuals before and after EKF processing, together with a brief discussion of potential unmodeled effects such as polarization drift (mitigated in our setup by polarization-maintaining fiber) and any residual non-Gaussian components. These additions will make the attribution of the threefold improvement more transparent while preserving the experimental data already presented. revision: partial

Circularity Check

0 steps flagged

No significant circularity; results are empirical outcomes of applied EKF

full rationale

The paper presents an EKF that incorporates modeled nonlinearity and noise statistics to process interferometric intensity signals into temperature estimates. Reported performance (2.21e-5 K simulation error, 8.34e-5 K experimental resolution) is obtained by running the filter on simulated and measured data, not by algebraic reduction of the output to the model inputs. No self-definitional equations, fitted parameters renamed as predictions, or load-bearing self-citations appear in the derivation. The method is externally validated against conventional inversion on the same data streams, satisfying the criterion for self-contained, non-circular claims.

Axiom & Free-Parameter Ledger

1 free parameters · 1 axioms · 0 invented entities

The method's performance hinges on the validity of the system model and noise assumptions, which are not specified in detail in the abstract.

free parameters (1)
  • Process and measurement noise covariance matrices
    EKF requires specification of noise statistics, which are typically tuned or fitted to match observed system behavior.
axioms (1)
  • domain assumption The interferometric signal intensity has a known nonlinear dependence on temperature and can be modeled with additive noise.
    This is the basis for applying the EKF to invert the signal for temperature.

pith-pipeline@v0.9.0 · 5475 in / 1127 out tokens · 36580 ms · 2026-05-08T07:20:15.460769+00:00 · methodology

discussion (0)

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Reference graph

Works this paper leans on

3 extracted references · 3 canonical work pages

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    26Ying T. Chen. Use of single-mode optical fiber in the stabilization of laser frequency.Applied Optics, 28(11):2017,

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    De- sign of temperature measurement system guided by thermal dis- sipation coefficient of ntc thermistor.Sensors and Actuators A: Physical, 377:115772, October 2024

    33Ziang Liu, Peng Huo, Chenyu Shi, Yuquan Yan, Fanlin Kong, Shiyu Cao, Aimin Chang, Junhua Wang, and Jincheng Yao. De- sign of temperature measurement system guided by thermal dis- sipation coefficient of ntc thermistor.Sensors and Actuators A: Physical, 377:115772, October 2024

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