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.

To fuse multiple sensors or use other localization algorithms, see Localization and Pose Estimation.

Functions

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Accelerometers

accelparams Accelerometer sensor parameters

Gyroscopes

`allanvar`	Allan variance
`gyroparams`	Gyroscope sensor parameters

Magnetometers

`magparams`	Magnetometer sensor parameters
`magcal`	Magnetometer calibration coefficients

Satellites

`gnssconstellation`	Satellite locations at specified time
`lookangles`	Satellite look angles from receiver and satellite positions
`pseudoranges`	Pseudoranges between GNSS receiver and satellites
`receiverposition`	Estimate GNSS receiver position and velocity
`skyplot`	Plot satellite azimuth and elevation data
`extractNMEASentence`	Verify and extract NMEA sentence data into string array
`rinexread`	Read data from RINEX file
`rinexinfo`	Get information about RINEX file
`semread`	Read data from SEM almanac file

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 system and GNSS/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 and manufacturer-specific NMEA sentences sent from marine electronic devices
`gpsdev`	Connect to a GPS receiver connected to host computer

Blocks

IMU	IMU simulation model
INS	Simulate INS sensor
GPS	Simulate GPS sensor readings with noise

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.
Simulate Inertial Sensor Readings from a Driving Scenario
Generate synthetic sensor data from IMU, GPS, and wheel encoders using driving scenario generation tools from Automated Driving Toolbox™.
Analyze GPS Satellite Visibility
This example shows how to simulate and analyze GPS satellite visibility at specified receiver positions and times.
Simulate GPS Sensor Noise
This example shows how to use the GPS block to add GPS sensor noise to position and velocity inputs in Simulink®.
Simulate INS Block
In this example, you simulate an INS block by using the pose information of a vehicle undertaking a left-turn trajectory.

Featured Examples

Introduction to Simulating IMU Measurements

Simulate inertial measurement unit (IMU) measurements using the imuSensor 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, accelparams, and magparams, 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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Gesture Recognition Using Inertial Measurement Units

Recognize gestures based on a handheld inertial measurement unit (IMU). Gesture recognition is a subfield of the general Human Activity Recognition (HAR) field. In this example, you use quaternion dynamic time warping and clustering to build a template matching algorithm to classify five gestures.

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Detect Multipath GPS Reading Errors Using Residual Filtering in Inertial Sensor Fusion

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. These receiver position estimates come from 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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Estimate GNSS Receiver Position with Simulated Satellite Constellations

Track the position of a ground vehicle using a simulated Global Navigation Satellite System (GNSS) receiver. The satellites are simulated using the satelliteScenario object, the satellite signal processing of the receiver are simulated using the lookangles and pseudoranges functions, and the receiver position is estimated with the receiverposition function.

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Ground Vehicle Pose Estimation for Tightly Coupled IMU and GNSS

Estimate the position and orientation of a ground vehicle by building a tightly coupled extended Kalman filter and using it to fuse sensor measurements. A tightly coupled filter fuses inertial measurement unit (IMU) readings with raw global navigation satellite system (GNSS) readings. In contrast, a loosely coupled filter fuses IMU readings with filtered GNSS receiver readings.

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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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Plot Position of GNSS Receiver Using Live NMEA Data or NMEA Log File

Parse information from NMEA sentences and use the obtained information to plot the location. In this example, you use the nmeaParser system object available in Navigation Toolbox to:

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End-to-End GPS Legacy Navigation Receiver Using C/A-Code

Estimate the global positioning system (GPS) receiver position using a multi-satellite GPS baseband waveform. You use the receiver independent exchange format (RINEX) and an almanac file to model the GPS constellation and generate a multi-satellite baseband waveform. Simulate the satellite scenario to get relative positions of satellites with respect to the modeled receiver. For this satellite scenario, model the Doppler shift, delay, and received signal power. Based on these calculations, impair the generated baseband signal with Doppler shift, delay, and noise. This example shows how to estimate the simulated receiver position from this impaired GPS baseband signal.

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