Sensor Fusion and Tracking Toolbox 시작하기

Introduces how to generate synthetic radar detections in a tracking scenario that simulates target motion and sensor detections. Specifically, this example shows:

Use trackingScenarioDesigner with an existing session file. Using the application, you can add, modify, or remove platforms, monostatic radar sensors, and trajectories of all objects in the scenario. You can also export the scenario as a MATLAB script for further analysis.

Model different radar scan modes using the fusionRadarSensor. This example shows how to configure the fusionRadarSensor for several commonly used radar scan modes. With this model, you can simulate radars which mechanically scan, electronically scan, and which use both mechanical and electronic scanning. The scan limits in azimuth and elevation are configurable for both mechanical and electronic scan modes.

Use 6-axis and 9-axis fusion algorithms to compute orientation. There are several algorithms to compute orientation from inertial measurement units (IMUs) and magnetic-angular rate-gravity (MARG) units. This example covers the basics of orientation and how to use these algorithms.

Track maneuvering targets using various tracking filters. The example shows the difference between filters that use a single motion model and multiple motion models.

Tune a tracking filter and improve the tracking performance of the tracker.

Analyze recorded truth data to model the motion of truth objects and configure a filter to track them.

These examples show how to convert actual detections in the native format of the sensor into objectDetection objects. objectDetection is the standard input format for most tracking filters and trackers in the toolbox. The six examples progressively show how to set up objectDetection with varied tracking scenarios.

Tune and run a tracker to track multiple objects in the scene. The example explains and demonstrates the importance of key properties of the trackers in the Sensor Fusion and Tracking Toolbox.