magparams

Magnetometer sensor parameters

Since R2018b

Description

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

Creation

Syntax

params = magarams

params = magparams(Name,Value)

Description

params = magarams returns an ideal magnetometer sensor parameters object with default values.

params = magparams(Name,Value) configures magparams 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 (μT)

Inf (default) | real positive scalar

Maximum sensor reading in μT, specified as a real positive scalar.

Data Types: single | double

Resolution — Resolution of sensor measurements (μT/LSB)

0 (default) | real nonnegative scalar

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

Data Types: single | double

ConstantBias — Constant sensor offset bias (μT)

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

Constant sensor offset bias in μT, 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 in the range [0,100] | 3-element row vector in the range [0,100] | 3-by-3 matrix in the range [0,100]

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 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 (μT/√Hz)

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

Power spectral density of sensor noise in μT/√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

BiasInstability — Instability of the bias offset (μT)

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

Instability of the bias offset in μT, 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 (μT/√Hz)

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

Integrated white noise of sensor in (μT/√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

TemperatureBias — Sensor bias from temperature (μT/℃)

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

Sensor bias from temperature in (μT/℃), 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 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 Magnetometer Data from Stationary Inputs

Generate magnetometer data for an imuSensor object from stationary inputs.

Generate a magnetometer parameter object with a maximum sensor reading of 1200 $$\mu T$$ and a resolution of 0.1 $$\mu T/LSB$$. The constant offset bias is 1 $$\mu T$$. The sensor has a power spectral density of$\left(\frac{\left[0.60.60.9\right]}{\sqrt{100}}\right)$ $$\mu T/\sqrt{Hz}$$. The bias from temperature is [0.8 0.8 2.4] $$\mu T/{}^{0}C$$. The scale factor error from temperature is 0.1 %$$/{}^{0}C$$.

params = magparams('MeasurementRange',1200,'Resolution',0.1,'ConstantBias',1,'NoiseDensity',[0.6 0.6 0.9]/sqrt(100),'TemperatureBias',[0.8 0.8 2.4],'TemperatureScaleFactor',0.1);

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

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

imu = imuSensor('accel-mag','SampleRate', Fs, 'Magnetometer', params);

Generate magnetometer data from the imuSensor object.

orient = quaternion.ones(numSamples, 1);
acc = zeros(numSamples, 3);
angvel = zeros(numSamples, 3);
 
[~, magData] = imu(acc, angvel, orient);

Plot the resultant magnetometer data.

plot(t, magData)
title('Magnetometer')
xlabel('s')
ylabel('\mu T')

Extended Capabilities

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

Version History

Introduced in R2018b