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.
- Orientation, Position, and Coordinate Convention
Learn about toolbox conventions for spatial representation and coordinate systems.
- 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 the
theaterPlotobject to visualize various aspects of a tracking scenario.
Tracking Scenario and Sensors
Inertial Sensor Fusion
Metrics and Visualization
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.