These examples track targets in airspace for air traffic control and general radar surveillance.
Air Traffic Control
Generate an air traffic control scenario, simulate radar detections from an airport surveillance radar (ASR), and configure a global nearest neighbor (GNN) tracker to track the simulated targets using the radar detections. This enables you to evaluate different target scenarios, radar requirements, and tracker configurations without needing access to costly aircraft or equipment. This example covers the entire synthetic data workflow.
Simulate and Track En-Route Aircraft in Earth-Centered Scenarios
Use an Earth-Centered trackingScenario and a geoTrajectory object to model a flight trajectory that spans thousands of kilometers. You use two different models to generate synthetic detections of the airplane: a monostatic radar and ADS-B reports. You use a multi-object tracker to estimate the plane trajectory, compare the tracking performance, and explore the impact that ADS-B provides on the overall tracking quality.
Simulate, Detect, and Track Anomalies in a Landing Approach
The example shows how to automatically detect deviations and anomalies in aircraft making final approaches to an airport runway. In this example, you will model an ideal landing approach trajectory and generate variants from it, simulate radar tracks, and issue warnings as soon as the tracks deviate from safe landing rules.
Multiplatform Radar Detection Fusion
Fuse radar detections from a multiplatform radar network. The network includes two airborne and one ground-based long-range radar platforms. See the Multiplatform Radar Detection Generation example for details. A central tracker processes the detections from all platforms at a fixed update interval. This enables you to evaluate the network's performance against target types, platform maneuvers, as well as platform configurations and locations..
Adaptive Tracking of Maneuvering Targets with Managed Radar
Use radar resource management to efficiently track multiple maneuvering targets. Tracking maneuvering targets requires the radar to revisit the targets more frequently than tracking non-maneuvering targets. An interacting multiple model (IMM) filter estimates when the target is maneuvering. This estimate helps to manage the radar revisit time and therefore enhances the tracking. This example uses the Radar Toolbox™ for the radar model and Sensor Fusion and Tracking Toolbox™ for the tracking.
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