imuSensor and Allan Variance

Hello everyone,

I am creating an IMU simulation using the built-in imuSensor model in MATLAB. The block includes several parameters that define IMU noise characteristics, but I do not fully understand how these parameters relate to Allan variance–derived noise coefficients.

Here is the list of gyroscope parameters available in

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gyroparams with properties:

MeasurementRange: Inf rad/s

Resolution: 0 (rad/s)/LSB

ConstantBias: [0 0 0] rad/s

AxesMisalignment: [3⨯3 double] %

NoiseDensity: [0 0 0] (rad/s)/√Hz

BiasInstability: [0 0 0] rad/s

RandomWalk: [0 0 0] (rad/s)*√Hz

NoiseType: "double-sided"

BiasInstabilityCoefficients: [1⨯1 struct]

TemperatureBias: [0 0 0] (rad/s)/°C

TemperatureScaleFactor: [0 0 0] %/°C

AccelerationBias: [0 0 0] (rad/s)/(m/s²)

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I have estimated my sensor noise parameters from Allan variance analysis, specifically:

• ARW (N)
• Bias Instability (B)
• Rate Random Walk (K)

My goal is to correctly map these Allan variance parameters N, B, and K to the corresponding imuSensor block parameters:

• NoiseDensity
• BiasInstability
• RandomWalk

I would appreciate clarification on how these quantities correspond mathematically and physically, and how to correctly convert Allan variance results into the parameters expected by MATLAB’s IMU sensor model.

Answer 1

[Ritika Thusoo]

Hi,

Allan variance helps you identify key IMU noise parameters: Angle Random Walk, Bias Instability, and Rate Random Walk by analysing how gyroscope noise behaves over different averaging times. For more information, please refer to the example in the following MATLAB documentation:

https://www.mathworks.com/help/fusion/ug/inertial-sensor-noise-analysis-using-allan-variance.html