Radar and Camera Tracking

Track highway vehicles around an ego vehicle as extended objects that span multiple sensor resolution cells.

Construct an asynchronous sensor fusion and tracking model in Simulink®.

Generate embedded code for a JPDA tracker and verify it using processor-in-the-loop (PIL) simulations.

The challenges associated with tracking vehicles on a highway in the presence of multipath radar reflections. It also shows a ghost filtering approach used with an extended object tracker to simultaneously filter ghost detections and track objects.

Track highway vehicles around an ego vehicle in Simulink. In this example, you use multiple extended object tracking techniques to track highway vehicles and evaluate their tracking performance. This example closely follows the Extended Object Tracking of Highway Vehicles with Radar and Camera MATLAB® example.

Model and mitigate multipath radar reflections in a highway driving scenario in Simulink®. It closely follows the Highway Vehicle Tracking with Multipath Radar Reflections MATLAB® example.

Track moving objects with multiple high-resolution radars using a grid-based tracker. A grid-based tracker enables early fusion of data from high-resolution sensors such as radars and lidars to estimate a dynamic occupancy grid map and a global object list. For grid-based tracking with lidar sensors refer to the Grid-Based Tracking in Urban Environments Using Multiple Lidars