accelparams

Accelerometer sensor parameters

Description

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

Creation

Syntax

params = accelparams

params = accelparams(Name,Value)

Description

params = accelparams returns an ideal accelerometer sensor parameters object with default values.

params = accelparams(Name,Value) configures an accelerometer sensor parameters 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

MeasurementRange — Maximum sensor reading (m/s²)

inf (default) | real positive scalar

Maximum sensor reading in m/s², specified as a real positive scalar.

Data Types: single | double

Resolution — Resolution of sensor measurements ((m/s²)/LSB)

0 (default) | real nonnegative scalar

Resolution of sensor measurements in (m/s²)/LSB, specified as a real nonnegative scalar. Here, LSB is the acronym for least significant bit. Resolution is often referred to as Scale Factor for accelerometer.

Data Types: single | double

ConstantBias — Constant sensor offset bias (m/s²)

[0 0 0] (default) | real scalar | real 3-element row vector

Constant sensor offset bias in 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

AxesMisalignment — Sensor axes skew (%)

diag([100 100 100]) (default) | scalar | 3-element row vector | 3-by-3 matrix

Sensor axes skew in percentage, specified as a scalar, a 3-element row vector, or a 3-by-3 matrix. 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 v_measure is obtained from the true state v_true via the misalignment matrix as:

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

• 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

NoiseDensity — Power spectral density of sensor noise (m/s²/√Hz)

[0 0 0] (default) | real scalar | real 3-element row vector

Power spectral density of sensor noise in (m/s²/√Hz), specified as a real scalar or 3-element row vector. This property corresponds to the velocity random walk (VRW). Any scalar input is converted into a real 3-element row vector where each element has the input scalar value.

Data Types: single | double

BiasInstabilityCoefficients — Filter coefficients for bias instability noise generation

fractalcoef (default) | structure

Filter coefficients for bias instability noise generation, specified as a structure. The structure has these fields:

• Numerator — Numerator coefficients, specified as a real-valued vector.

• Denominator — Denominator coefficients, specified as a real-valued vector.

To specify coefficients for fractal noise, use the fractalcoef function.

Example: struct(Numerator=1,Denominator=[1 -0.5])

Data Types: struct

BiasInstability — Instability of the bias offset (m/s²)

[0 0 0] (default) | real scalar | real 3-element row vector

Instability of the bias offset in 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

RandomWalk — Integrated white noise of sensor ((m/s²)*(√Hz))

[0 0 0] (default) | real scalar | real 3-element row vector

Integrated white noise of sensor in (m/s²)*(√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

NoiseType — Type of random noise

"double-sided" (default) | "single-sided"

Type of random noise, specified as:

• "double-sided" — Random noise coefficients have a scale factor of 2.

• "single-sided" — Random noise coefficients have a scale factor of 1.

Data Types: char | string

TemperatureBias — Sensor bias from temperature ((m/s²)/℃)

[0 0 0] (default) | real scalar | real 3-element row vector

Sensor bias from temperature in (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

TemperatureScaleFactor — Scale factor error from temperature (%/℃)

[0 0 0] (default) | real scalar in the range [0,100] | real 3-element row vector in the range [0,100]

Scale factor error from temperature in %/℃, specified as a real scalar or real 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

Examples

Generate Accelerometer Data from Stationary Inputs

Generate accelerometer data for an imuSensor object from stationary inputs.

Generate an accelerometer parameter object with a maximum sensor reading of 19.6 $\mathrm{m}/{\mathrm{s}}^2$ and a resolution of 0.598 $\left(\mathrm{mm}/{\mathrm{s}}^2\right)/\mathrm{LSB}$. The constant offset bias is 0.49 $$m/s^2$$. The sensor has a power spectral density of 3920 $\left(\mu \mathrm{m}/{\mathrm{s}}^2\right)/\sqrt{\mathrm{Hz}}$. The bias from temperature is 0.294 $\left(\mathrm{m}/{\mathrm{s}}^2\right)$$$/{}^{0}C$$. The scale factor error from temperature is 0.02%$$/{}^{0}C$$. The sensor axes are skewed by 2%.

params = accelparams('MeasurementRange',19.6,'Resolution',0.598e-3,'ConstantBias',0.49,'NoiseDensity',3920e-6,'TemperatureBias',0.294,'TemperatureScaleFactor',0.02,'AxesMisalignment',2);

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

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

imu = imuSensor('SampleRate', Fs, 'Accelerometer', params);

Generate accelerometer data from the imuSensor object.

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

Plot the resultant accelerometer data.

plot(t, accelData)
title('Accelerometer')
xlabel('s')
ylabel('m/s^2')

Extended Capabilities

C/C++ Code Generation
Generate C and C++ code using MATLAB® Coder™.

Version History

Introduced in R2018b

R2023b: Specify bias instability noise coefficients and noise type

You can use the new BiasInstabilityCoefficients property of the accelparams object to specify the coefficients of the transfer function used to generate the bias instability noise. Additionally, you can use the new NoiseType property of this object to set the scale factor of the noise coefficients as 1 or 2.