Sensor Models

Calibration and simulation for IMU, GPS, and range sensors

Perform sensor modeling and simulation for accelerometers, magnetometers, gyroscopes, altimeters, GPS, IMU, and range sensors. Analyze sensor readings, sensor noise, environmental conditions and other configuration parameters. Generate trajectories to emulate these sensors traveling through a world and calibrate the performance of your sensors.

Functions

`allanvar`	Allan variance
`magcal`	Magnetometer calibration coefficients
`accelparams`	Accelerometer sensor parameters
`gyroparams`	Gyroscope sensor parameters
`magparams`	Magnetometer sensor parameters

Objects

`gnssSensor`	Simulate GNSS to generate position and velocity readings
`altimeterSensor`	Altimeter simulation model
`gpsSensor`	GPS receiver simulation model
`imuSensor`	IMU simulation model
`insSensor`	Inertial navigation and GPS simulation model
`rangeSensor`	Simulate range-bearing sensor readings
`wheelEncoderUnicycle`	Simulate wheel encoder sensor readings for unicycle vehicle
`wheelEncoderBicycle`	Simulate wheel encoder sensor readings for bicycle vehicle
`wheelEncoderDifferentialDrive`	Simulate wheel encoder sensor readings for differential drive vehicle
`wheelEncoderAckermann`	Simulate wheel encoder sensor readings for Ackermann vehicle
`kinematicTrajectory`	Rate-driven trajectory generator
`timescope`	Display time-domain signals
`waypointTrajectory`	Waypoint trajectory generator
`nmeaParser`	Parse data from standard NMEA sentences sent from GNSS receivers
`gpsdev`	Connect to a GPS receiver connected to host computer

Blocks

AHRS	Orientation from accelerometer, gyroscope, and magnetometer readings
IMU	IMU simulation model
INS	Simulate INS sensor

Topics

Model IMU, GPS, and INS/GPS

Model combinations of inertial sensors and GPS

Inertial Sensor Noise Analysis Using Allan Variance

This example shows how to use the Allan variance to determine noise parameters of a MEMS gyroscope.

Wheel Encoder Error Sources

Explore the various error sources of wheel encoders and how they affect the wheel odometry estimate.

Remove Bias from Angular Velocity Measurement

This example shows how to remove gyroscope bias from an IMU using imufilter.

Configure Time Scope MATLAB Object

Customize timescope properties and use measurement tools.

Featured Examples

Introduction to Simulating IMU Measurements

Simulate inertial measurement unit (IMU) measurements using the imuSensor (Sensor Fusion and Tracking Toolbox) System object. An IMU can include a combination of individual sensors, including a gyroscope, an accelerometer, and a magnetometer. You can specify properties of the individual sensors using gyroparams (Sensor Fusion and Tracking Toolbox), accelparams (Sensor Fusion and Tracking Toolbox), and magparams (Sensor Fusion and Tracking Toolbox), respectively.

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Magnetometer Calibration

Magnetometers detect magnetic field strength along a sensor's X,Y and Z axes. Accurate magnetic field measurements are essential for sensor fusion and the determination of heading and orientation.

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Generate Off-Centered IMU Readings

Generate inertial measurement unit (IMU) readings from a sensor that is mounted on a ground vehicle. Depending on the location of the sensor, the IMU accelerations are different.

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Detect Noise in Sensor Readings with Residual Filtering

Use the residualgps object function and residual filtering to detect when new sensor measurements may not be consistent with the current filter state.

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GNSS Simulation Overview

Global Navigation Satellite System (GNSS) simulation generates receiver position estimates. Generate GNSS receiver position estimates using GPS and GNSS sensor models as gpsSensor and gnssSensor objects. Monitor the status of the position estimate in the gnssSensor using the dilution of precision outputs and compare the number of satellites available.

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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.

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