# gyroparams

Gyroscope sensor parameters

## Description

The gyroparams class creates a gyroscope sensor parameters object. You can use this object to model a gyroscope when simulating an IMU with imuSensor. See the Algorithms section of imuSensor for details of gyroparams modeling.

## Creation

### Description

params = gyroparams returns an ideal gyroscope sensor parameters object with default values.

params = gyroparams(Name,Value) configures gyroparams object properties using one or more Name,Value pair arguments. Name is a property name and Value is the corresponding value. Name must appear inside single quotes (''). You can specify several name-value pair arguments in any order as Name1,Value1,...,NameN,ValueN. Any unspecified properties take default values.

## Properties

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Data Types: single | double

Resolution of sensor measurements in (rad/s)/LSB, specified as a real nonnegative scalar. Here, LSB is the acronym for least significant bit.

Data Types: single | double

Constant sensor offset bias in rad/s, specified as a real scalar or 3-element row vector. Any scalar input is converted into a real 3-element row vector where each element has the input scalar value.

Data Types: single | double

Sensor axes skew in percentage, specified as a scalar, a 3-element row vector, or a 3-by-3 matrix with values ranging from 0 to 100. The diagonal elements of the matrix account for the misalignment effects for each axes. The off-diagonal elements account for the cross-axes misalignment effects. The measured state vmeasure is obtained from the true state vmeasure via the misalignment matrix as:

${v}_{measure}=\frac{1}{100}M\text{ }{v}_{true}=\frac{1}{100}\left[\begin{array}{ccc}{m}_{11}& {m}_{12}& {m}_{13}\\ {m}_{21}& {m}_{22}& {m}_{23}\\ {m}_{31}& {m}_{32}& {m}_{33}\end{array}\right]{v}_{true}$

• If you specify the property as a scalar, then all the off-diagonal elements of the matrix take the value of the specified scalar and all the diagonal elements are 100.

• If you specify the property as a vector [a b c], then m21 = m31 = a, m12 = m32 = b, and m13 = m23 = c. All the diagonal elements are 100.

Data Types: single | double

Power spectral density of sensor noise in (rad/s)/√Hz, specified as a real scalar or 3-element row vector. This property corresponds to the angle random walk (ARW). Any scalar input is converted into a real 3-element row vector where each element has the input scalar value.

Data Types: single | double

Instability of the bias offset in rad/s, specified as a real scalar or 3-element row vector. Any scalar input is converted into a real 3-element row vector where each element has the input scalar value.

Data Types: single | double

Integrated white noise of sensor in (rad/s)(√Hz), specified as a real scalar or 3-element row vector. Any scalar input is converted into a real 3-element row vector where each element has the input scalar value.

Data Types: single | double

Sensor bias from temperature in ((rad/s)/℃), specified as a real scalar or 3-element row vector. Any scalar input is converted into a real 3-element row vector where each element has the input scalar value.

Data Types: single | double

Scale factor error from temperature in (%/℃), specified as a real scalar or 3-element row vector with values ranging from 0 to 100. Any scalar input is converted into a real 3-element row vector where each element has the input scalar value.

Data Types: single | double

Sensor bias from linear acceleration in (rad/s)/(m/s2), specified as a real scalar or 3-element row vector. Any scalar input is converted into a real 3-element row vector where each element has the input scalar value.

Data Types: single | double

## Examples

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Generate gyroscope data for an imuSensor object from stationary inputs.

Generate a gyroscope parameter object with a maximum sensor reading of 4.363 $\mathrm{rad}/\mathrm{s}$ and a resolution of 1.332e-4 $\left(\mathrm{rad}/\mathrm{s}\right)/\mathrm{LSB}$. The constant offset bias is 0.349 $\mathrm{rad}/\mathrm{s}$. The sensor has a power spectral density of 8.727e-4 . The bias from temperature is 0.349 $\left(\mathrm{rad}/{\mathrm{s}}^{2}\right)$$/{}^{0}C$. The scale factor error from temperature is 0.2%$/{}^{0}C$. The sensor axes are skewed by 2%. The sensor bias from linear acceleration is 0.178e-3 $\left(rad/s\right)/\left(m/{s}^{2}\right)$

params = gyroparams('MeasurementRange',4.363,'Resolution',1.332e-04,'ConstantBias',0.349,'NoiseDensity',8.727e-4,'TemperatureBias',0.349,'TemperatureScaleFactor',0.02,'AxesMisalignment',2,'AccelerationBias',0.178e-3);

Use a sample rate of 100 Hz spaced out over 1000 samples. Create the imuSensor object using the gyroscope parameter object.

Fs = 100;
numSamples = 1000;
t = 0:1/Fs:(numSamples-1)/Fs;

imu = imuSensor('accel-gyro','SampleRate', Fs, 'Gyroscope', params);

Generate gyroscope data from the imuSensor object.

orient = quaternion.ones(numSamples, 1);
acc = zeros(numSamples, 3);
angvel = zeros(numSamples, 3);

[~, gyroData] = imu(acc, angvel, orient);

Plot the resultant gyroscope data.

plot(t, gyroData)
title('Gyroscope')
xlabel('s')