IMU

IMU simulation model

Library:
Navigation Toolbox Toolbox / Multisensor Positioning / Sensor Models
Sensor Fusion and Tracking Toolbox / Multisensor Positioning / Sensor Models

Description

The IMU Simulink^® block models receiving data from an inertial measurement unit (IMU) composed of accelerometer, gyroscope, and magnetometer sensors.

Ports

Input

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`Linear Acceleration` — Acceleration of IMU in local navigation coordinate system (m/s²)
N-by-3 matrix of real scalar

Acceleration of the IMU in the local navigation coordinate system, specified as an N-by-3 matrix of real scalars in meters per second squared. N is the number of samples in the current frame.

Data Types: single | double

`Angular Velocity` — Angular velocity of IMU in local navigation coordinate system (rad/s)
N-by-3 matrix of real scalar

Angular velocity of the IMU sensor body frame in the local navigation coordinate system, specified as an N-by-3 matrix of scalars in radians per second. N is the number of samples in the current frame.

Data Types: single | double

`Orientation` — Orientation of IMU in local navigation coordinate system
N-by-4 array of real scalar | 3-by-3-by-N-element rotation matrix

Orientation of the IMU sensor body frame with respect to the local navigation coordinate system, specified as an N-by-4 array of real scalars or a 3-by-3-by-N rotation matrix. Each row the of the N-by-4 array is assumed to be the four elements of a quaternion (Sensor Fusion and Tracking Toolbox). N is the number of samples in the current frame.

Data Types: single | double

Output

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`Accel` — Accelerometer measurement of IMU in sensor body coordinate system (m/s²)
N-by-3 matrix of real scalar

Accelerometer measurement of the IMU in the sensor body coordinate system, returned as an N-by-3 matrix of real scalars in meters per second squared. N is the number of samples in the current frame.

Data Types: single | double

`Gyro` — Gyroscope measurement of IMU in sensor body coordinate system (rad/s)
N-by-3 matrix of real scalar

Gyroscope measurement of the IMU in the sensor body coordinate system, returned as an N-by-3 matrix of real scalars in radians per second. N is the number of samples in the current frame.

Data Types: single | double

`Mag` — Magnetometer measurement of IMU in sensor body coordinate system (μT)
N-by-3 matrix of real scalar

Magnetometer measurement of the IMU in the sensor body coordinate system, returned as an N-by-3 matrix of real scalars in microtesla. N is the number of samples in the current frame.

Data Types: single | double

Parameters

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Parameters

`Reference frame` — Navigation reference frame
`NED` (default) | `ENU`

Navigation reference frame, specified as NED (North-East-Down) or ENU (East-North-Up).

`Temperature (^oC)` — Operating temperature of IMU (^oC)
`25` (default) | real scalar

Operating temperature of the IMU in degrees Celsius, specified as a real scalar.

When the block calculates temperature scale factors and environmental drift noises, 25 ^oC is used as the nominal temperature.

Data Types: single | double

`Magnetic field (NED)` — Magnetic field vector expressed in NED navigation frame (μT)
`[27.5550, -2.4169, -16.0849]` (default) | 1-by-3 vector of scalar

Magnetic field vector expressed in the NED navigation frame, specified as a 1-by-3 vector of scalars.

The default magnetic field corresponds to the magnetic field at latitude zero, longitude zero, and altitude zero.

Dependencies

To enable this parameter, set Reference frame to NED.

Data Types: single | double

`MagneticField (ENU)` — Magnetic field vector expressed in ENU navigation frame (μT)
`[-2.4169, 27.5550, 16.0849]` (default) | 1-by-3 vector of scalar

Magnetic field vector expressed in the ENU navigation frame, specified as a 1-by-3 vector of scalars.

The default magnetic field corresponds to the magnetic field at latitude zero, longitude zero, and altitude zero.

Dependencies

To enable this parameter, set Reference frame to ENU.

Data Types: single | double

`Seed` — Initial seed for randomization
`67` (default) | nonnegative integer

Initial seed of a random number generator algorithm, specified as a nonnegative integer.

Data Types: single | double

`Simulate using` — Type of simulation to run
`Interpreted Execution` (default) | `Code Generation`

Interpreted execution — Simulate the model using the MATLAB^® interpreter. This option shortens startup time. In Interpreted execution mode, you can debug the source code of the block.
Code generation — Simulate the model using generated C code. The first time that you run a simulation, Simulink generates C code for the block. The C code is reused for subsequent simulations if the model does not change. This option requires additional startup time.

Accelerometer

`Maximum readings (m/s²)` — 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 ((m/s²)/LSB)` — 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.

Data Types: single | double

`Constant offset bias (m/s²)` — 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

`Axis skew (%)` — Sensor axes skew (%)
`[0 0 0]` (default) | real scalar in the range [0,100] | real 3-element row vector in the range [0,100]

Sensor axes skew in a percentage, 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

`Velocity random walk (m/s²/√Hz)` — Velocity random walk (m/s²/√Hz)
`[0 0 0]` (default) | real scalar | real 3-element row vector

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

Data Types: single | double

`Bias Instability (m/s²)` — 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

`Acceleration random walk ((m/s²)(√Hz))` — Acceleration random walk ((m/s²)(√Hz))
`[0 0 0]` (default) | real scalar | real 3-element row vector

Acceleration random walk 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

`Bias from temperature ((m/s²)/℃)` — 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

`Temperature scale factor (%/℃)` — 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

Gyroscope

`Maximum readings (rad/s)` — Maximum sensor reading (rad/s)
`inf` (default) | real positive scalar

Maximum sensor reading in rad/s, specified as a real positive scalar.

Data Types: single | double

`Resolution ((rad/s)/LSB)` — Resolution of sensor measurements ((rad/s)/LSB)
`0` (default) | real nonnegative scalar

Resolution of sensor measurements in (rad/s)/LSB, specified as a real nonnegative scalar.

Data Types: single | double

`Constant offset bias (rad/s)` — Constant sensor offset bias (rad/s)
`[0 0 0]` (default) | real scalar | real 3-element row vector

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

`Axis skew (%)` — Sensor axes skew (%)
`[0 0 0]` (default) | real scalar in the range [0,100] | real 3-element row vector in the range [0,100]

Data Types: single | double

`Bias from acceleration ((rad/s)/(m/s²)` — Sensor bias from linear acceleration (rad/s)/(m/s²)
`[0 0 0]` (default) | real scalar | real 3-element row vector

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

`Angle random walk ((rad/s)/(√Hz))` — Acceleration random walk ((rad/s)/(√Hz))
`[0 0 0]` (default) | real scalar | real 3-element row vector

Acceleration random walk 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

`Bias Instability (rad/s)` — Instability of the bias offset (rad/s)
`[0 0 0]` (default) | real scalar | real 3-element row vector

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

`Rate random walk ((rad/s)(√Hz))` — Integrated white noise of sensor ((rad/s)(√Hz))
`[0 0 0]` (default) | real scalar | real 3-element row vector

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

`Bias from temperature ((rad/s)/℃)` — Sensor bias from temperature ((rad/s)/℃)
`[0 0 0]` (default) | real scalar | real 3-element row vector

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

`Temperature scale factor (%/℃)` — 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]

Data Types: single | double

Magnetometer

`Maximum readings (μT)` — 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 ((μT)/LSB)` — Resolution of sensor measurements ((μT)/LSB)
`0` (default) | real nonnegative scalar

Resolution of sensor measurements in (μT)/LSB, specified as a real nonnegative scalar.

Data Types: single | double

`Constant offset bias (μT)` — 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

`Axis skew (%)` — Sensor axes skew (%)
`[0 0 0]` (default) | real scalar in the range [0,100] | real 3-element row vector in the range [0,100]

Data Types: single | double

`White noise PSD (μT/√Hz)` — 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

`Bias Instability (μT)` — 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

`Random walk ((μT)√Hz)` — 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

`Bias from temperature (μT/℃)` — 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

`Temperature scale factor (%/℃)` — 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]

Data Types: single | double

Model Examples

IMU Sensor Fusion with Simulink

Generate and fuse IMU sensor data using Simulink®. You can accurately model the behavior of an accelerometer, a gyroscope, and a magnetometer and fuse their outputs to compute orientation.

Algorithms

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Accelerometer

The accelerometer model uses the ground-truth orientation and acceleration inputs and the imuSensor and accelparams (Sensor Fusion and Tracking Toolbox) properties to model accelerometer readings.

Obtain Total Acceleration

To obtain the total acceleration (totalAcc), the acceleration is preprocessed by negating and adding the gravity constant vector (g= [0; 0; 9.8] m/s²) as:

$t o t a l A c c = - a c c e l e r a t i o n + g$

Convert to Sensor Frame

Then the total acceleration is converted from the local navigation frame to the sensor frame using:

$a = (o r i e n t a t i o n) {(t o t a l A c c)}^{T}$

If the orientation is input in quaternion form, it is converted to a rotation matrix before processing.

Bulk Model

The ground-truth acceleration in the sensor frame, a, passes through the bulk model, which adds axes misalignment and bias:

$b = {([\begin{matrix} 1 & \frac{α_{2}}{100} & \frac{α_{3}}{100} \\ \frac{α_{1}}{100} & 1 & \frac{α_{3}}{100} \\ \frac{α_{1}}{100} & \frac{α_{2}}{100} & 1 \end{matrix}] (a^{T}))}^{T} + ConstantBias$

where ConstantBias (Sensor Fusion and Tracking Toolbox) is a property of accelparams (Sensor Fusion and Tracking Toolbox), and α₁, α₂, and α₃ are given by the first, second, and third elements of the AxesMisalignment (Sensor Fusion and Tracking Toolbox) property of accelparams (Sensor Fusion and Tracking Toolbox).

Bias Instability Drift

The bias instability drift is modeled as white noise biased and then filtered:

$β_{1} = h_{1} * (w) (BiasInstability)$

where BiasInstability (Sensor Fusion and Tracking Toolbox) is a property of accelparams (Sensor Fusion and Tracking Toolbox), and h₁ is a filter defined by the SampleRate (Sensor Fusion and Tracking Toolbox) property:

$H_{1} (z) = \frac{1}{1 - \frac{1}{2} z^{- 1}}$

White Noise Drift

White noise drift is modeled by multiplying elements of the white noise random stream by the standard deviation:

$β_{2} = (w) (\sqrt{\frac{SampleRate}{2}}) (NoiseDensity)$

where SampleRate (Sensor Fusion and Tracking Toolbox) is an imuSensor property, and NoiseDensity (Sensor Fusion and Tracking Toolbox) is an accelparams (Sensor Fusion and Tracking Toolbox) property. Elements of w are random numbers given by settings of the imuSensor random stream.

Random Walk Drift

The random walk drift is modeled by biasing elements of the white noise random stream and then filtering:

$β_{3} = h_{2} * (w) (\frac{RandomWalk}{\sqrt{\frac{SampleRate}{2}}})$

where RandomWalk (Sensor Fusion and Tracking Toolbox) is a property of accelparams (Sensor Fusion and Tracking Toolbox), SampleRate (Sensor Fusion and Tracking Toolbox) is a property of imuSensor, and h₂ is a filter defined as:

$H_{2} (z) = \frac{1}{1 - z^{- 1}}$

Environmental Drift Noise

The environmental drift noise is modeled by multiplying the temperature difference from a standard with the temperature bias:

$Δ_{e} = (Temperature - 25) (TemperatureBias)$

where Temperature (Sensor Fusion and Tracking Toolbox) is a property of imuSensor, and TemperatureBias (Sensor Fusion and Tracking Toolbox) is a property of accelparams (Sensor Fusion and Tracking Toolbox). The constant 25 corresponds to a standard temperature.

Scale Factor Error Model

The temperature scale factor error is modeled as:

$s c a l e F a c t o r E r r o r = 1 + (\frac{Temperature - 25}{100}) (TemperatureScaleFactor)$

where Temperature (Sensor Fusion and Tracking Toolbox) is a property of imuSensor, and TemperatureScaleFactor (Sensor Fusion and Tracking Toolbox) is a property of accelparams (Sensor Fusion and Tracking Toolbox). The constant 25 corresponds to a standard temperature.

Quantization Model

The quantization is modeled by first saturating the continuous signal model:

$e = {\begin{matrix} \begin{matrix} MeasurementRange \\ - MeasurementRange \end{matrix} \\ d \end{matrix} \begin{matrix} \end{matrix} \begin{matrix} if \\ if \\ else \end{matrix} \begin{matrix} \end{matrix} \begin{matrix} d > MeasurementRange \\ - d > MeasurementRange \end{matrix}$

and then setting the resolution:

$a c c e l R e a d i n g s = (Resolution) (round (\frac{e}{Resolution}))$

where MeasurementRange (Sensor Fusion and Tracking Toolbox) is a property of accelparams (Sensor Fusion and Tracking Toolbox).

Gyroscope

The gyroscope model uses the ground-truth orientation, acceleration, and angular velocity inputs, and the imuSensor and gyroparams (Sensor Fusion and Tracking Toolbox) properties to model accelerometer readings.

Convert to Sensor Frame

The ground-truth angular velocity is converted from the local frame to the sensor frame using the ground-truth orientation:

$a = (o r i e n t a t i o n) {(a n g u l a r V e l o c i t y)}^{T}$

If the orientation is input in quaternion form, it is converted to a rotation matrix before processing.

Bulk Model

The ground-truth angular velocity in the sensor frame, a, passes through the bulk model, which adds axes misalignment and bias:

where ConstantBias (Sensor Fusion and Tracking Toolbox) is a property of gyroparams (Sensor Fusion and Tracking Toolbox), and α₁, α₂, and α₃ are given by the first, second, and third elements of the AxesMisalignment (Sensor Fusion and Tracking Toolbox) property of gyroparams (Sensor Fusion and Tracking Toolbox).

Bias Instability Drift

The bias instability drift is modeled as white noise biased and then filtered:

$β_{1} = h_{1} * (w) (BiasInstability)$

where BiasInstability (Sensor Fusion and Tracking Toolbox) is a property of gyroparams (Sensor Fusion and Tracking Toolbox) and h₁ is a filter defined by the SampleRate (Sensor Fusion and Tracking Toolbox) property:

$H_{1} (z) = \frac{1}{1 - \frac{1}{2} z^{- 1}}$

White Noise Drift

White noise drift is modeled by multiplying elements of the white noise random stream by the standard deviation:

$β_{2} = (w) (\sqrt{\frac{SampleRate}{2}}) (NoiseDensity)$

where SampleRate (Sensor Fusion and Tracking Toolbox) is an imuSensor property, and NoiseDensity (Sensor Fusion and Tracking Toolbox) is an gyroparams (Sensor Fusion and Tracking Toolbox) property. The elements of w are random numbers given by settings of the imuSensor random stream.

Random Walk Drift

The random walk drift is modeled by biasing elements of the white noise random stream and then filtering:

$β_{3} = h_{2} * (w) (\frac{RandomWalk}{\sqrt{\frac{SampleRate}{2}}})$

where RandomWalk (Sensor Fusion and Tracking Toolbox) is a property of gyroparams (Sensor Fusion and Tracking Toolbox), SampleRate (Sensor Fusion and Tracking Toolbox) is a property of imuSensor, and h₂ is a filter defined as:

$H_{2} (z) = \frac{1}{1 - z^{- 1}}$

Environmental Drift Noise

The environmental drift noise is modeled by multiplying the temperature difference from a standard with the temperature bias:

$Δ_{e} = (Temperature - 25) (TemperatureBias)$

where Temperature (Sensor Fusion and Tracking Toolbox) is a property of imuSensor, and TemperatureBias (Sensor Fusion and Tracking Toolbox) is a property of gyroparams (Sensor Fusion and Tracking Toolbox). The constant 25 corresponds to a standard temperature.

Scale Factor Error Model

The temperature scale factor error is modeled as:

$s c a l e F a c t o r E r r o r = 1 + (\frac{Temperature - 25}{100}) (TemperatureScaleFactor)$

where Temperature (Sensor Fusion and Tracking Toolbox) is a property of imuSensor, and TemperatureScaleFactor (Sensor Fusion and Tracking Toolbox) is a property of gyroparams (Sensor Fusion and Tracking Toolbox). The constant 25 corresponds to a standard temperature.

Quantization Model

The quantization is modeled by first saturating the continuous signal model:

and then setting the resolution:

$g y r o R e a d i n g s = (Resolution) (round (\frac{e}{Resolution}))$

where MeasurementRange (Sensor Fusion and Tracking Toolbox) is a property of gyroparams (Sensor Fusion and Tracking Toolbox).

Magnetometer

The magnetometer model uses the ground-truth orientation and acceleration inputs, and the imuSensor and magparams (Sensor Fusion and Tracking Toolbox) properties to model magnetometer readings.

Convert to Sensor Frame

The ground-truth acceleration is converted from the local frame to the sensor frame using the ground-truth orientation:

$a = (o r i e n t a t i o n) {(t o t a l A c c)}^{T}$

If the orientation is input in quaternion form, it is converted to a rotation matrix before processing.

Bulk Model

The ground-truth acceleration in the sensor frame, a, passes through the bulk model, which adds axes misalignment and bias:

where ConstantBias (Sensor Fusion and Tracking Toolbox) is a property of magparams (Sensor Fusion and Tracking Toolbox), and α₁, α₂, and α₃ are given by the first, second, and third elements of the AxesMisalignment (Sensor Fusion and Tracking Toolbox) property of magparams (Sensor Fusion and Tracking Toolbox).

Bias Instability Drift

The bias instability drift is modeled as white noise biased and then filtered:

$β_{1} = h_{1} * (w) (BiasInstability)$

where BiasInstability (Sensor Fusion and Tracking Toolbox) is a property of magparams (Sensor Fusion and Tracking Toolbox) and h₁ is a filter defined by the SampleRate (Sensor Fusion and Tracking Toolbox) property:

$H_{1} (z) = \frac{1}{1 - \frac{1}{2} z^{- 1}}$

White Noise Drift

White noise drift is modeled by multiplying elements of the white noise random stream by the standard deviation:

$β_{2} = (w) (\sqrt{\frac{SampleRate}{2}}) (NoiseDensity)$

where SampleRate (Sensor Fusion and Tracking Toolbox) is an imuSensor property, and NoiseDensity (Sensor Fusion and Tracking Toolbox) is an magparams (Sensor Fusion and Tracking Toolbox) property. The elements of w are random numbers given by settings of the imuSensor random stream.

Random Walk Drift

The random walk drift is modeled by biasing elements of the white noise random stream and then filtering:

$β_{3} = h_{2} * (w) (\frac{RandomWalk}{\sqrt{\frac{SampleRate}{2}}})$

where RandomWalk (Sensor Fusion and Tracking Toolbox) is a property of magparams (Sensor Fusion and Tracking Toolbox), SampleRate (Sensor Fusion and Tracking Toolbox) is a property of imuSensor, and h₂ is a filter defined as:

$H_{2} (z) = \frac{1}{1 - z^{- 1}}$

Environmental Drift Noise

The environmental drift noise is modeled by multiplying the temperature difference from a standard with the temperature bias:

$Δ_{e} = (Temperature - 25) (TemperatureBias)$

where Temperature (Sensor Fusion and Tracking Toolbox) is a property of imuSensor, and TemperatureBias (Sensor Fusion and Tracking Toolbox) is a property of magparams (Sensor Fusion and Tracking Toolbox). The constant 25 corresponds to a standard temperature.

Scale Factor Error Model

The temperature scale factor error is modeled as:

$s c a l e F a c t o r E r r o r = 1 + (\frac{Temperature - 25}{100}) (TemperatureScaleFactor)$

where Temperature (Sensor Fusion and Tracking Toolbox) is a property of imuSensor, and TemperatureScaleFactor (Sensor Fusion and Tracking Toolbox) is a property of magparams (Sensor Fusion and Tracking Toolbox). The constant 25 corresponds to a standard temperature.

Quantization Model

The quantization is modeled by first saturating the continuous signal model:

and then setting the resolution:

$m a g R e a d i n g s = (Resolution) (round (\frac{e}{Resolution}))$

where MeasurementRange (Sensor Fusion and Tracking Toolbox) is a property of magparams (Sensor Fusion and Tracking Toolbox).

Documentation

IMU

Description

Ports

Input

Linear Acceleration — Acceleration of IMU in local navigation coordinate system (m/s2) N-by-3 matrix of real scalar

Angular Velocity — Angular velocity of IMU in local navigation coordinate system (rad/s) N-by-3 matrix of real scalar

Orientation — Orientation of IMU in local navigation coordinate system N-by-4 array of real scalar | 3-by-3-by-N-element rotation matrix

Output

Accel — Accelerometer measurement of IMU in sensor body coordinate system (m/s2) N-by-3 matrix of real scalar

Gyro — Gyroscope measurement of IMU in sensor body coordinate system (rad/s) N-by-3 matrix of real scalar

Mag — Magnetometer measurement of IMU in sensor body coordinate system (μT) N-by-3 matrix of real scalar

Parameters

Reference frame — Navigation reference frame NED (default) | ENU

Temperature (oC) — Operating temperature of IMU (oC) 25 (default) | real scalar

Magnetic field (NED) — Magnetic field vector expressed in NED navigation frame (μT) [27.5550, -2.4169, -16.0849] (default) | 1-by-3 vector of scalar

Dependencies

MagneticField (ENU) — Magnetic field vector expressed in ENU navigation frame (μT) [-2.4169, 27.5550, 16.0849] (default) | 1-by-3 vector of scalar

Dependencies

Seed — Initial seed for randomization 67 (default) | nonnegative integer

Simulate using — Type of simulation to run Interpreted Execution (default) | Code Generation

Maximum readings (m/s2) — Maximum sensor reading (m/s2) inf (default) | real positive scalar

Resolution ((m/s2)/LSB) — Resolution of sensor measurements ((m/s2)/LSB) 0 (default) | real nonnegative scalar

Constant offset bias (m/s2) — Constant sensor offset bias (m/s2) [0 0 0] (default) | real scalar | real 3-element row vector

Axis skew (%) — Sensor axes skew (%) [0 0 0] (default) | real scalar in the range [0,100] | real 3-element row vector in the range [0,100]

Velocity random walk (m/s2/√Hz) — Velocity random walk (m/s2/√Hz) [0 0 0] (default) | real scalar | real 3-element row vector

Bias Instability (m/s2) — Instability of the bias offset (m/s2) [0 0 0] (default) | real scalar | real 3-element row vector

Acceleration random walk ((m/s2)(√Hz)) — Acceleration random walk ((m/s2)(√Hz)) [0 0 0] (default) | real scalar | real 3-element row vector

Bias from temperature ((m/s2)/℃) — Sensor bias from temperature ((m/s2)/℃) [0 0 0] (default) | real scalar | real 3-element row vector

Temperature scale factor (%/℃) — 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]

Maximum readings (rad/s) — Maximum sensor reading (rad/s) inf (default) | real positive scalar

Resolution ((rad/s)/LSB) — Resolution of sensor measurements ((rad/s)/LSB) 0 (default) | real nonnegative scalar

Constant offset bias (rad/s) — Constant sensor offset bias (rad/s) [0 0 0] (default) | real scalar | real 3-element row vector

Axis skew (%) — Sensor axes skew (%) [0 0 0] (default) | real scalar in the range [0,100] | real 3-element row vector in the range [0,100]

Bias from acceleration ((rad/s)/(m/s2) — Sensor bias from linear acceleration (rad/s)/(m/s2) [0 0 0] (default) | real scalar | real 3-element row vector

Angle random walk ((rad/s)/(√Hz)) — Acceleration random walk ((rad/s)/(√Hz)) [0 0 0] (default) | real scalar | real 3-element row vector

Bias Instability (rad/s) — Instability of the bias offset (rad/s) [0 0 0] (default) | real scalar | real 3-element row vector

Rate random walk ((rad/s)(√Hz)) — Integrated white noise of sensor ((rad/s)(√Hz)) [0 0 0] (default) | real scalar | real 3-element row vector

Bias from temperature ((rad/s)/℃) — Sensor bias from temperature ((rad/s)/℃) [0 0 0] (default) | real scalar | real 3-element row vector

Temperature scale factor (%/℃) — 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]

Maximum readings (μT) — Maximum sensor reading (μT) inf (default) | real positive scalar

Resolution ((μT)/LSB) — Resolution of sensor measurements ((μT)/LSB) 0 (default) | real nonnegative scalar

Constant offset bias (μT) — Constant sensor offset bias (μT) [0 0 0] (default) | real scalar | real 3-element row vector

Axis skew (%) — Sensor axes skew (%) [0 0 0] (default) | real scalar in the range [0,100] | real 3-element row vector in the range [0,100]

White noise PSD (μT/√Hz) — Power spectral density of sensor noise (μT/√Hz) [0 0 0] (default) | real scalar | real 3-element row vector

Bias Instability (μT) — Instability of the bias offset (μT) [0 0 0] (default) | real scalar | real 3-element row vector

Random walk ((μT)*√Hz) — Integrated white noise of sensor ((μT)*√Hz) [0 0 0] (default) | real scalar | real 3-element row vector

Bias from temperature (μT/℃) — Sensor bias from temperature (μT/℃) [0 0 0] (default) | real scalar | real 3-element row vector

Temperature scale factor (%/℃) — 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]

Model Examples

IMU Sensor Fusion with Simulink

Algorithms

Accelerometer

Gyroscope

Magnetometer

Extended Capabilities

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

See Also

Classes

System Objects

Navigation Toolbox Documentation

Support

`Linear Acceleration` — Acceleration of IMU in local navigation coordinate system (m/s²)
N-by-3 matrix of real scalar

`Angular Velocity` — Angular velocity of IMU in local navigation coordinate system (rad/s)
N-by-3 matrix of real scalar

`Orientation` — Orientation of IMU in local navigation coordinate system
N-by-4 array of real scalar | 3-by-3-by-N-element rotation matrix

`Accel` — Accelerometer measurement of IMU in sensor body coordinate system (m/s²)
N-by-3 matrix of real scalar

`Gyro` — Gyroscope measurement of IMU in sensor body coordinate system (rad/s)
N-by-3 matrix of real scalar

`Mag` — Magnetometer measurement of IMU in sensor body coordinate system (μT)
N-by-3 matrix of real scalar

`Reference frame` — Navigation reference frame
`NED` (default) | `ENU`

`Temperature (^oC)` — Operating temperature of IMU (^oC)
`25` (default) | real scalar

`Magnetic field (NED)` — Magnetic field vector expressed in NED navigation frame (μT)
`[27.5550, -2.4169, -16.0849]` (default) | 1-by-3 vector of scalar

`MagneticField (ENU)` — Magnetic field vector expressed in ENU navigation frame (μT)
`[-2.4169, 27.5550, 16.0849]` (default) | 1-by-3 vector of scalar

`Seed` — Initial seed for randomization
`67` (default) | nonnegative integer

`Simulate using` — Type of simulation to run
`Interpreted Execution` (default) | `Code Generation`

`Maximum readings (m/s²)` — Maximum sensor reading (m/s²)
`inf` (default) | real positive scalar

`Resolution ((m/s²)/LSB)` — Resolution of sensor measurements ((m/s²)/LSB)
`0` (default) | real nonnegative scalar

`Constant offset bias (m/s²)` — Constant sensor offset bias (m/s²)
`[0 0 0]` (default) | real scalar | real 3-element row vector

`Axis skew (%)` — Sensor axes skew (%)
`[0 0 0]` (default) | real scalar in the range [0,100] | real 3-element row vector in the range [0,100]

`Velocity random walk (m/s²/√Hz)` — Velocity random walk (m/s²/√Hz)
`[0 0 0]` (default) | real scalar | real 3-element row vector

`Bias Instability (m/s²)` — Instability of the bias offset (m/s²)
`[0 0 0]` (default) | real scalar | real 3-element row vector

`Acceleration random walk ((m/s²)(√Hz))` — Acceleration random walk ((m/s²)(√Hz))
`[0 0 0]` (default) | real scalar | real 3-element row vector

`Bias from temperature ((m/s²)/℃)` — Sensor bias from temperature ((m/s²)/℃)
`[0 0 0]` (default) | real scalar | real 3-element row vector

`Temperature scale factor (%/℃)` — 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]

`Maximum readings (rad/s)` — Maximum sensor reading (rad/s)
`inf` (default) | real positive scalar

`Resolution ((rad/s)/LSB)` — Resolution of sensor measurements ((rad/s)/LSB)
`0` (default) | real nonnegative scalar

`Constant offset bias (rad/s)` — Constant sensor offset bias (rad/s)
`[0 0 0]` (default) | real scalar | real 3-element row vector

`Axis skew (%)` — Sensor axes skew (%)
`[0 0 0]` (default) | real scalar in the range [0,100] | real 3-element row vector in the range [0,100]

`Bias from acceleration ((rad/s)/(m/s²)` — Sensor bias from linear acceleration (rad/s)/(m/s²)
`[0 0 0]` (default) | real scalar | real 3-element row vector

`Angle random walk ((rad/s)/(√Hz))` — Acceleration random walk ((rad/s)/(√Hz))
`[0 0 0]` (default) | real scalar | real 3-element row vector

`Bias Instability (rad/s)` — Instability of the bias offset (rad/s)
`[0 0 0]` (default) | real scalar | real 3-element row vector

`Rate random walk ((rad/s)(√Hz))` — Integrated white noise of sensor ((rad/s)(√Hz))
`[0 0 0]` (default) | real scalar | real 3-element row vector

`Bias from temperature ((rad/s)/℃)` — Sensor bias from temperature ((rad/s)/℃)
`[0 0 0]` (default) | real scalar | real 3-element row vector

`Temperature scale factor (%/℃)` — 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]

`Maximum readings (μT)` — Maximum sensor reading (μT)
`inf` (default) | real positive scalar

`Resolution ((μT)/LSB)` — Resolution of sensor measurements ((μT)/LSB)
`0` (default) | real nonnegative scalar

`Constant offset bias (μT)` — Constant sensor offset bias (μT)
`[0 0 0]` (default) | real scalar | real 3-element row vector

`Axis skew (%)` — Sensor axes skew (%)
`[0 0 0]` (default) | real scalar in the range [0,100] | real 3-element row vector in the range [0,100]

`White noise PSD (μT/√Hz)` — Power spectral density of sensor noise (μT/√Hz)
`[0 0 0]` (default) | real scalar | real 3-element row vector

`Bias Instability (μT)` — Instability of the bias offset (μT)
`[0 0 0]` (default) | real scalar | real 3-element row vector

`Random walk ((μT)√Hz)` — Integrated white noise of sensor ((μT)√Hz)
`[0 0 0]` (default) | real scalar | real 3-element row vector

`Bias from temperature (μT/℃)` — Sensor bias from temperature (μT/℃)
`[0 0 0]` (default) | real scalar | real 3-element row vector

`Temperature scale factor (%/℃)` — 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]

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