Get Started with Sensor Fusion and Tracking Toolbox
Sensor Fusion and Tracking Toolbox™ includes algorithms and tools for designing, simulating, and testing systems that fuse data from multiple sensors to maintain situational awareness and localization. Reference examples provide a starting point for multi-object tracking and sensor fusion development for surveillance and autonomous systems, including airborne, spaceborne, ground-based, shipborne, and underwater systems.
You can fuse data from real-world sensors, including active and passive radar, sonar, lidar, EO/IR, IMU, and GPS. You can also generate synthetic data from virtual sensors to test your algorithms under different scenarios. The toolbox includes multi-object trackers and estimation filters for evaluating architectures that combine grid-level, detection-level, and object- or track-level fusion. It also provides metrics, including OSPA and GOSPA, for validating performance against ground truth scenes.
For simulation acceleration or rapid prototyping, the toolbox supports C and C++ code generation.
Tutorials
- Orientation, Position, and Coordinate Convention
Learn about toolbox conventions for spatial representation and coordinate systems. - Tracking Simulation Overview
You can build a complete tracking simulation using the functions and objects supplied in this toolbox. - Simulate Radar Detections
Simulate target detections by radar sensors. - Model IMU, GPS, and INS/GPS
Model combinations of inertial sensors and GPS. - Introduction to Estimation Filters
General review of estimation filters provided in the toolbox. - Introduction to Multiple Target Tracking
Introduction to assignment-based multiple target trackers. - Introduction to Tracking Metrics
While designing a multi-object tracking system, it is essential to devise a method to evaluate its performance against the available ground truth. - Use theaterPlot to Visualize Tracking Scenario
This example shows how to use thetheaterPlot
object to visualize various aspects of a tracking scenario.
Toolbox Conventions
Tracking Scenario and Sensors
Inertial Sensor Fusion
Estimation Filters
Multi-Object Tracking
Metrics and Visualization
Featured Examples
Videos
Part 1: What is Sensor Fusion?
An overview of what sensor fusion is and how it helps in the
design of autonomous systems.
Part 2: Fusing Mag, Accel, and Gyro to Estimate Orientation
Use magnetometer, accelerometer, and gyro to estimate an object’s
orientation.
Part 3: Fusing GPS and IMU to Estimate Pose
Use GPS and an IMU to estimate an object’s orientation and
position.
Part 4: Tracking a Single Object With an IMM Filter
Track a single object by estimating state with an interacting
multiple model filter.
Part 5: How to Track Multiple Objects at Once?
Introduce two common problems in multi object tracking: Data
association and track maintenance.
Part 6: What is Track-Level Fusion?
Introduce track-to-track fusion and tracking architecture.