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Research ArticleOriginal Article
Open Access

Conservative Estimation of Inertial Sensor Errors Using Allan Variance Data

Kyle A. Lethander and Clark N. Taylor
NAVIGATION: Journal of the Institute of Navigation September 2023, 70 (3) navi.563; DOI: https://doi.org/10.33012/navi.563
Kyle A. Lethander
1Mechanical Engineering, California Institute of Technology, California, USA
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Clark N. Taylor
1Mechanical Engineering, California Institute of Technology, California, USA
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  • For correspondence: [email protected] [email protected]
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  • FIGURE 1
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    FIGURE 1

    AVSM model fitted to Allan variance data. The estimated noise coefficients (generally) bound truth, but the overestimation is not a tight overbound.

  • FIGURE 2
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    FIGURE 2

    ARMAV model fitted to Allan variance data: This fit was generated using an unconstrained nonlinear regression (Equation [11]) using scipy.optimize.

  • FIGURE 3
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    FIGURE 3

    (a) Allan variance plots of simulated data against truth (b) 95% confidence upper bound of simulated data Allan variance against truth

  • FIGURE 4
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    FIGURE 4

    An example of constrained optimization using the ARMAV cost function: This model maintains a least-squares minimization, but at every observation time τ, the modeled Allan variance is greater than or equal to the measured Allan variance.

  • FIGURE 5
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    FIGURE 5

    This figure illustrates the technique used for evaluating each estimation technique; detailed explanations of the different transitions and circles can be found in the text.

  • FIGURE 6
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    FIGURE 6

    A comparison of ARMAV and GMWM, both with and without weighting, at four different τ values: The top-left and bottom-right plots correspond with the smallest and largest τ values

  • FIGURE 7
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    FIGURE 7

    A comparison of the ARMAV-based weighted, constrained, and constrained with χ2 overbounding estimation techniques; the y-axis represents the values Embedded Image for each given tau value (x-axis).

  • FIGURE 8
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    FIGURE 8

    A comparison of the GMWM-based weighted, constrained, and constrained with χ2 overbounding estimation techniques. The y-axis represents the values Embedded Image for each given tau value (x-axis).

  • FIGURE 9
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    FIGURE 9

    Analog Devices ADIS16470 MEMS IMU: The device footprint measures approximately 1.25″ × 1.35″.

  • FIGURE 10
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    FIGURE 10

    ADIS16470 comparison of datasheet (black dashed line) with measured Allan variance for x, y, and z axes: 20 runs (same sensor) of measured Allan variance are given by the thin lines.

  • FIGURE 11
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    FIGURE 11

    Comparison of Allan variances (x-axis gyro) between different sensors of the same model: Each thin line represents a different sensor, but all are of the same model (ADIS16470). This shows a greater variation than that found for a single sensor (e.g., Figure 10).

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    TABLE 1

    Sources of Noise With Associated Allan Variance (AV) Slopes

    Error MechanismAV effectEmbedded Image slopeUnits
    (gyro.)
    Units
    (accel.)
    τread
    QuantizationEmbedded Image−2deg2m2s−2Embedded Image
    Random walkβrwτ−1/2−1deg2 s−1m2 s−31
    Bias instabilityEmbedded Image0deg2 s−2m2 s−4argmin Embedded Image
    Rate random walkEmbedded Image+1deg2 s−3m2 s−53
    Rate rampEmbedded Image+2deg2 s−4m2 s−6Embedded Image
    • View popup
    TABLE 2

    χ2 Simulation Results

    Number of trials1001001,0001,00010,00010,000
    Percent overbound94.6294.3494.5994.8694.9794.97
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    TABLE 3

    Noise Coefficients Selected for Monte-Carlo Simulation

    Error MechanismMagnitudeUnits
    Quantization, σq1.0 × 10−7deg
    Random walk, σrw4.0 × 10−3deg s−1/2
    Bias instability, σb1.0 × 10−3deg s−1
    Rate random walk, σrrw2.0 × 10−4deg s−3/2
    Rate ramp, σrr1.0 × 10−8deg s−2
    • View popup
    TABLE 4

    A Quantitative Comparison Between ARMAV and GMWM

    1 hour3 hours5 hours
    Method% belowRMSE log% belowRMSE log% belowRMSE log
    ARMAV weighted74.34 %0.0982.38 %0.0983.88 %0.08
    Constrained ARMAV15.02 %0.3517.66 %0.2918.91 %0.28
    C-ARMAV w/ χ20.39 %0.390.51 %0.520.42 %0.44
    GMWM weighted75.98 %0.1083.14 %0.1084.37 %0.09
    Constrained GMWM16.34 %0.1716.78 %0.1117.01 %0.13
    C-GMWM w/ χ20.58 %0.280.38 %0.270.43 %0.19
    • View popup
    TABLE 5

    ADIS16470 Calculated Noise Coefficient Statistics: Random Walk, Bias Instability, and Rate Random Walk

    Measured SignalNoise CoefficientDatasheetGMWMCGMWMCGMWM w/ χ2 upper bound
    Embedded ImagesEmbedded ImagesEmbedded Images
    x-axis gyroσrw (deg s−1/2)5.67 × 10−33.66 × 10−31.30 × 10−53.77 × 10−32.97 × 10−53.77 × 10−35.96 × 10−5
    σb (deg s−1)2.22 × 10−35.03 × 10−41.64 × 10−42.11 × 10−42.05 × 10−45.01 × 10−42.12 × 10−3
    σrrw (deg s−3/2)–4.02 × 10−45.62 × 10−54.94 × 10−43.31 × 10−57.42 × 10−45.21 × 10−4
    y-axis gyroσrw5.67 × 10−32.73 × 10−31.16 × 10−52.73 × 10−31.52 × 10−52.73 × 10−31.41 × 10−5
    σb2.22 × 10−31.30 × 10−37.39 × 10−52.20 × 10−37.09 × 10−42.14 × 10−35.67 × 10−4
    σrrw–1.29 × 10−42.73 × 10−54.89 × 10−41.93 × 10−45.74 × 10−41.65 × 10−4
    z-axis gyroσrw5.67 × 10−33.90 × 10−34.68 × 10−63.91 × 10−31.40 × 10−53.93 × 10−32.13 × 10−5
    σb2.22 × 10−31.11 × 10−36.01 × 10−51.33 × 10−32.39 × 10−41.30 × 10−31.72 × 10−4
    σrrw–2.61 × 10−44.15 × 10−53.13 × 10−41.81 × 10−54.94 × 10−42.44 × 10−4
    • View popup
    TABLE 6

    Percent Overbound Between Models Generated From and the Allan Variance for 20 Data Collects of a Single Sensor

    AxisGMWM Overbound %CGMWM + χ2 overbound %
    x43.59%98.13%
    y51.34%90.13%
    z47.28%99.12%

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NAVIGATION: Journal of the Institute of Navigation: 70 (3)
NAVIGATION: Journal of the Institute of Navigation
Vol. 70, Issue 3
Fall 2023
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Conservative Estimation of Inertial Sensor Errors Using Allan Variance Data
Kyle A. Lethander, Clark N. Taylor
NAVIGATION: Journal of the Institute of Navigation Sep 2023, 70 (3) navi.563; DOI: 10.33012/navi.563

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Conservative Estimation of Inertial Sensor Errors Using Allan Variance Data
Kyle A. Lethander, Clark N. Taylor
NAVIGATION: Journal of the Institute of Navigation Sep 2023, 70 (3) navi.563; DOI: 10.33012/navi.563
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Keywords

  • allan variance
  • conservative estimation
  • inertial sensing

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