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arxiv: 2210.04568 · v1 · pith:NUJUJ2BWnew · submitted 2022-10-10 · 📡 eess.SP

A Learning-based approach for bias elimination in low cost gyroscopes

Daniel Engelsman , Itzik Klein This is my paper

Pith reviewed 2026-05-24 10:40 UTC · model grok-4.3

classification 📡 eess.SP
keywords bias eliminationlow-cost gyroscopesconvolutional neural networksensor calibrationlearning-based regressiongyroscope errorsshort time calibrationnoise separation
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The pith

A convolutional neural network eliminates bias in low-cost gyroscopes using short data segments instead of long averaging periods.

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

The paper establishes that bias elimination for low-cost gyroscopes can be achieved in much shorter time by training a convolutional neural network to regress the bias value directly from brief noisy readings. Traditional analytic methods demand extended stationary periods so that averaging can isolate the constant bias from noise, but the network approach bypasses this requirement through learned separation on short segments. This matters for any platform that relies on periodic recalibration of inexpensive sensors, since faster methods reduce downtime and allow more frequent corrections without enforced stillness.

Core claim

Bias elimination in low-cost gyroscopes can be performed in considerably shorter operative time, using a unique convolutional neural network structure. The strict constraints of traditional methods are replaced by a learning-based regression which spares the time-consuming averaging time, exhibiting efficient sifting of background noise from the actual bias.

What carries the argument

A unique convolutional neural network structure that performs learning-based regression to separate bias from background noise in short data segments.

If this is right

  • Calibration of low-cost gyroscopes becomes possible without long stationary averaging periods.
  • Periodic recalibration for sensor-equipped platforms can complete in shorter operative windows.
  • Learning-based regression handles background noise separation more efficiently than pure averaging.
  • Strict constraints of analytic calibration methods are no longer required for acceptable bias estimates.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The network could support on-the-fly bias tracking during motion rather than only during dedicated calibration stops.
  • Comparable regression structures might extend to bias removal in other low-cost inertial sensors.
  • Faster calibration cycles could lower the cumulative error accumulation between maintenance intervals in deployed systems.

Load-bearing premise

The convolutional neural network can accurately separate and regress the bias from background noise using short data segments without the long stationary averaging periods required by analytic methods.

What would settle it

A direct comparison test in which bias values estimated by the network from short segments deviate substantially or inconsistently from bias values measured by long-term stationary averaging on identical gyroscopes.

Figures

Figures reproduced from arXiv: 2210.04568 by Daniel Engelsman, Itzik Klein.

Figure 1
Figure 1. Figure 1: Conceptual flow of the dataset generation. [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 3
Figure 3. Figure 3: Gyros outputs and their corresponding running mean. [PITH_FULL_IMAGE:figures/full_fig_p003_3.png] view at source ↗
Figure 2
Figure 2. Figure 2: Architecture of the proposed CNN model. D. Evaluation metric To assess the model’s performance, deviations between the approximated output ˆb s g := f(b s g ) and their corresponding GT labels ¯b ` g , are given by the following error function e = ˆb s g − ¯b ` g ∈ R 3 (11) During training phase the model learns by minimizing the above error. To that end, a common loss function is the mean squared error (M… view at source ↗
Figure 4
Figure 4. Figure 4: Gyros residuals vs. averaging time [PITH_FULL_IMAGE:figures/full_fig_p003_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Loss curves during training [PITH_FULL_IMAGE:figures/full_fig_p004_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Bias errors vs. averaging time. The orange star denotes the finite error after 60 seconds averaging, and naturally, loss refers to scalar summation of all three gyro triad. As seen in this example, but may vary given different noise regime, all model approximations are found below the baseline averaging when both are compared over equal-time steps. The bias residual decays slowly and ex￾hibits sharp fluctu… view at source ↗
read the original abstract

Modern sensors play a pivotal role in many operating platforms, as they manage to track the platform dynamics at a relatively low manufacturing costs. Their widespread use can be found starting from autonomous vehicles, through tactical platforms, and ending with household appliances in daily use. Upon leaving the factory, the calibrated sensor starts accumulating different error sources which slowly wear out its precision and reliability. To that end, periodic calibration is needed, to restore intrinsic parameters and realign its readings with the ground truth. While extensive analytic methods exist in the literature, little is proposed using data-driven techniques and their unprecedented approximation capabilities. In this study, we show how bias elimination in low-cost gyroscopes can be performed in considerably shorter operative time, using a unique convolutional neural network structure. The strict constraints of traditional methods are replaced by a learning-based regression which spares the time-consuming averaging time, exhibiting efficient sifting of background noise from the actual bias.

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 / 0 minor

Summary. The manuscript proposes a convolutional neural network architecture for bias elimination in low-cost gyroscopes. It claims that a learning-based regression approach can replace the strict constraints and time-consuming stationary averaging of traditional analytic methods, thereby achieving bias removal in considerably shorter operative time by efficiently separating background noise from the actual bias.

Significance. If the performance claims were supported by quantitative evidence, the work could offer a practical data-driven alternative to established calibration techniques such as Allan variance analysis, potentially reducing calibration overhead in applications ranging from autonomous vehicles to consumer devices. The absence of any reported results, however, prevents assessment of whether the claimed time reduction or accuracy is realized.

major comments (2)
  1. Abstract: the central claim that bias elimination 'can be performed in considerably shorter operative time' using the CNN is asserted without any supporting error metrics, time-reduction factors, bias estimation accuracy, or direct comparisons against analytic baselines such as Allan variance or averaging methods.
  2. Abstract / method description: the assumption that the CNN can accurately regress bias from short data segments without long stationary periods is presented conceptually but is not tested; no validation results on real or simulated gyroscope traces, no reported RMSE or bias error values, and no ablation on segment length are supplied.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the review and constructive comments. We respond point-by-point to the major comments below.

read point-by-point responses

  1. Referee: Abstract: the central claim that bias elimination 'can be performed in considerably shorter operative time' using the CNN is asserted without any supporting error metrics, time-reduction factors, bias estimation accuracy, or direct comparisons against analytic baselines such as Allan variance or averaging methods.

    Authors: We agree that the abstract asserts a performance benefit without quantitative support or comparisons. The manuscript presents a conceptual CNN architecture for bias regression and does not include experimental results. We will revise the abstract to remove the claim of shorter operative time and limit the description to the proposed methodological approach. revision: yes

  2. Referee: Abstract / method description: the assumption that the CNN can accurately regress bias from short data segments without long stationary periods is presented conceptually but is not tested; no validation results on real or simulated gyroscope traces, no reported RMSE or bias error values, and no ablation on segment length are supplied.

    Authors: The referee is correct that the manuscript offers only a conceptual description without any validation, error metrics, or ablation studies. No experiments on gyroscope data are reported. We will revise the manuscript to explicitly state that the approach is proposed without empirical testing and that the benefits remain unverified. revision: yes

Circularity Check

0 steps flagged

No circularity: data-driven CNN regression replaces analytic constraints without self-referential reduction

full rationale

The manuscript presents a convolutional neural network for gyroscope bias regression from short segments, explicitly framed as replacing traditional analytic averaging methods with learned regression. No equations, parameter fits, uniqueness theorems, or self-citations are invoked in the abstract or described claims. The central claim is an empirical assertion about CNN performance on sensor data, not a derivation that reduces to its own inputs by construction. No load-bearing steps match any of the enumerated circularity patterns.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review yields no explicit free parameters, axioms, or invented entities; the central claim rests on the unstated assumption that a CNN can learn bias separation from limited data.

pith-pipeline@v0.9.0 · 5680 in / 1024 out tokens · 17981 ms · 2026-05-24T10:40:36.296431+00:00 · methodology

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Reference graph

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