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    AI Overview
    Custom Fiber Optic Gyro (FOG) error modeling
    involves developing mathematical models to compensate for deterministic (bias, scale factor) and random errors , often using temperature-dependent RBF neural networks, Kalman filters, or TD-models to improve accuracy. Key approaches include modeling the non-linear relationship between voltage (
    u u
    𝑢
    ), temperature (
    T cap T
    𝑇
    ), and angular velocity error (
    Δ ω cap delta omega
    Δ 𝜔
    ) for open-loop systems.
    Key Error Modeling Approaches
    • Temperature-Dependent Modeling : Since scaling factor errors are highly sensitive to temperature, models are developed to map temperature changes to specific output deviations.
    • Neural Network Models (RBF) : Radial-Basis-Function (RBF) neural networks are used to model non-linear mapping between output voltage (
      u u
      𝑢
      ), temperature (
      T cap T
      𝑇
      ), and angular velocity error (
      Δ ω cap delta omega
      Δ 𝜔
      ).
    • TD-Model (Theoretical/Data Hybrid) : This approach uses a theoretical model as a regularizer, combined with small-sample data to mitigate overfitting and account for specific batch variations in FOG construction.
    • Kalman Filtering : Used for compensating random errors and integrating FOG data with other systems (like GPS).
    • Open-Loop Optimization : For open-loop FOGs, modeling the angular velocity error (
      Δ ω cap delta omega
      Δ 𝜔
      ) directly (rather than total velocity) improves sensitivity in compensation.
    Performance Metrics
    Custom models can significantly improve accuracy, with reported reductions in maximum, minimum, and mean square error by over
    96 % 96 %
    9 6 %
    . Advanced, compact FOGs utilizing specific components (e.g., 45° tilted-fiber-gratings) can achieve bias instability below
    0.1 ∘ / h 0.1 raised to the composed with power / h
    0 . 1 ∘ / ℎ
    and full temperature bias stability better than
    1 ∘ / h 1 raised to the composed with power / h
    1 ∘ / ℎ
    .
    Key Considerations for Customization
    • Data Acquisition: Accurate, high-density data logging of voltage vs. rotational rate (RPM) is essential to map non-linear responses.
    • Compensation Techniques: Integrating Kalman filters with detailed error models provides the most robust results.
    • System Identification: Identifying if the error is, for example, a scaling factor error or a random drift allows for more precise modeling.
    For applications involving specialized, low-cost or compact FOGs, modeling the specific, non-linear error characteristics related to temperature is essential for maximizing performance.
    • A New Open-Loop Fiber Optic Gyro Error Compensation Method ...
      Abstract. With the open-loop fiber optic gyro (OFOG) model, output voltage and angular velocity can effectively compensate OFOG er...
      National Institutes of Health (NIH) | (.gov)
    • Compensation of gyroscope errors and GPS/DR integration - IEEE Xplore
      Gyroscope errors can be characterized into two types of error, deterministic and random errors. Each error term is mathematically ...
      IEEE Xplore
    • Modeling and Prediction of Thermal Deformation Errors in Fiber ...
      Oct 11, 2023 — Subsequently, for different batches of fiber optic gyroscopes, while there may be process variations, the resulting characteristic...
      National Institutes of Health (NIH) | (.gov)
    • The temperature error modeling and compensation method of fiber optic gyroscope scaling factor
      Nov 24, 2021 — The temperature error of the scaling factor in a fiber optic gyroscope (FOG) can affect its performance. The main error caused by ...
      SPIE Digital Library
    • Compact and high-reliability fiber-optic open-loop gyroscope ...
      Jan 25, 2023 — The performance of an open-loop fiber-optic gyroscope is strongly dependent on the optical characteristics of its polarizer. Here ...
      Optica Publishing Group
    • A New Open-Loop Fiber Optic Gyro Error Compensation Method ...
      Feb 27, 2015 — The relationship involving u , ω z , ω c and Δ ω is shown in Figure 2. The line 1 denotes the relationship between u and ω c , and...
      MDPI
    • 3D printed Fiber Optic Gyroscope ( Sagnac Interferometer )
      Nov 24, 2015 — and uh the Bluetooth Bluetooth transmitter is on the left hand side that's the little black box sticking out so it's ready to go w...
      YouTube · plenum88
      2m
    • Equivalent ARMA model representation for RLG random errors
      Jan 1, 2000 — The errors have to be modeled and identified before the sensors are engaged in a system so that they are properly compensated or f...
      IEEE
    • The temperature error modeling and compensation method of fiber optic gyroscope scaling factor
      Nov 24, 2021 — FOG often needs to work in a wide temperature range, so the scaling factor needs to be modeled and compensated. Because of the tem...
      SPIE Digital Library
    • A New Open-Loop Fiber Optic Gyro Error Compensation Method ...
      Abstract. With the open-loop fiber optic gyro (OFOG) model, output voltage and angular velocity can effectively compensate OFOG er...
      National Institutes of Health (NIH) | (.gov)
    • Compensation of gyroscope errors and GPS/DR integration - IEEE Xplore
      Gyroscope errors can be characterized into two types of error, deterministic and random errors. Each error term is mathematically ...
      IEEE Xplore
    • Modeling and Prediction of Thermal Deformation Errors in Fiber ...
      Oct 11, 2023 — Subsequently, for different batches of fiber optic gyroscopes, while there may be process variations, the resulting characteristic...
      National Institutes of Health (NIH) | (.gov)
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