Sensor Fusion for Object Tracking in MATLAB

Objective: Fuse IMU and GPS sensor data to estimate position and orientation.

• Model measurements from accelerometers, gyroscopes, magnetometers, and GPS
• Fuse sensor data to estimate the pose in terms of position, velocity, and orientation
• Visualize the pose estimation and plot platforms and trajectories

Objective: Import and process detections or generate scenarios used in multi-object trackers.

• Preprocess and package collected sensor detections
• Create a tracking scenario with multiple sensors and platforms
• Define waypoint or kinematic trajectories
• Customize sensor parameters
• Generate detections used in sensor fusion algorithms

Objective: Select and tune filters and motion models based on tracking requirements.

• Evaluate filters against scenario requirements
• Compare and contrast different motion models
• Configure an Interacting Multiple Model (IMM) filter to track different maneuvers

Objective: Determine the appropriate data association method for different tracking situations.

• Select from among Global Nearest Neighbor (GNN), Joint Probabilistic Data Association (JPDA), Track-Oriented Multiple Hypothesis (TOMHT), and other data association methods
• Determine how multiple detections are assigned to different tracks

Objective: Create multi-object trackers to fuse information from multiple sensors such as vision, radar, and lidar.

• Configure trackers and parameters
• Perform track association and management
• Visualize the tracked objects

Objective: Create multi-object trackers and fusion systems that receive angle-only or range-only measurements from passive sensor systems.

• Triangulate multiple line-of-sight detections
• Perform static fusion of passive synchronous sensor detections
• Track with range-only measurements
• Track with angle-only measurements