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 code generation.
Learn about toolbox conventions for spatial representation and coordinate systems.
Radar Sensors and Detections
Simulate target detections by radar sensors.
You can define a tracking simulation by using the
You can build a complete tracking simulation using the functions and objects supplied in this toolbox.
Inertial Sensor Fusion
Model combinations of inertial sensors and GPS.
Fuse inertial measurement unit (IMU) readings to determine orientation.
General review of estimation filters provided in the toolbox.
Introduction to assignment-based multiple target trackers.
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.