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.

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.

Model a surveillance scenario in a mountainous region where terrain can occlude both ground and aerial vehicles from the surveillance radar. You define a tracking scenario with geo-referenced terrain data from a Digital Terrain Elevation Data (DTED) file, create trajectories following terrain, simulate the scenario, and track targets with a multi-object tracker.

Generate a marine scenario, simulate radar detections from a marine surveillance radar, and configure a multi-target Probability Hypothesis Density (PHD) tracker to estimate the location and size of the simulated ships using the radar detections.

Model earth-centric trajectories using custom motion models within trackingScenario, how to configure a fusionRadarSensor in monostatic mode to generate synthetic detections of space debris, and how to setup a multi-object tracker to track the simulated targets.

Import a Two-Line Element (TLE) file of a satellite constellation, simulate radar detections of the constellation, and track the constellation.

An apron is a defined area at the airport intended to accommodate aircraft for purposes of loading or unloading passengers, mail or cargo, fueling, parking or maintenance [1]. Airport aprons are usually highly dynamic and heterogeneous environments where apron personnel and vehicles operate in close proximity to each other. Due to such nature of the aprons, it presents a higher risk for ground handling accidents involving aircraft as well as ground personnel. Lidar-based surveillance systems at aprons have been proposed as an effective method to improve the situation picture and to serve as a measure to mitigate high risk at the aprons [2].

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.

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..

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.

Simulate bistatic range-only detections using four sensor-emitter pairs. In addition, this example demonstrates how to localize and track multiple targets using bistatic range-only measurements.

Track objects using time difference of arrival (TDOA). This example introduces the challenges of localization with TDOA measurements as well as algorithms and techniques that can be used for tracking single and multiple objects with TDOA techniques.

Fuse position measurements from radar and angle-only measurements from infrared sensors for marine surveillance.

Track objects using angle-only measurements from spatially distributed and asynchronous passive sensors.

Illustrates the tracking of objects using measurements from spatially-distributed and synchronous passive sensors. In the Passive Ranging Using a Single Maneuvering Sensor, you learned that passive measurements provide incomplete observability of a target's state and how a single sensor can be maneuvered to gain range information. Alternatively, multiple stationary sensors can also be used to gain observability. In this example, you will learn how to track multiple objects by fusing multiple detections from passive synchronous sensors.

Track points targets in dense clutter using a Gaussian mixture probability hypothesis density (GM-PHD) tracker using a constant velocity model.

Create a tracking system for an air traffic control center using a global nearest neighbor (GNN) tracker in Simulink.

Model a marine scenario, simulate detections from a marine surveillance radar, and configure a multi-target probability hypothesis density (PHD) tracker to estimate the location and size of simulated ships using the radar detections in Simulink®. Simulink provides a powerful environment for the modeling and simulation of dynamic systems and processes. This example closely follows the Extended Object Tracking With Radar For Marine Surveillance MATLAB® example.

Radars generally receive echoes from all surfaces in the signal path. These unwanted back-scattered signals or echoes generated from physical objects are called clutter. In a densely cluttered environment, missed detections and false alarms make tracking objects a challenging task for conventional trackers such as Global Nearest-Neighbor (GNN) tracker. In such an environment a PHD tracker provides better estimation of objects as it can handle multiple detections per object per sensor without clustering them first. This example shows you how to track points targets in dense clutter using a Gaussian mixture probability hypothesis density (GM-PHD) tracker with a constant velocity model in Simulink. The example closely follows the Track Point Targets in Dense Clutter Using GM-PHD Tracker MATLAB® example.