生成空气交通管制方案，从机场监视雷达（ASR）模拟雷达检测，并配置全局最近的邻居（GNN）跟踪器，以使用雷达检测来跟踪模拟目标。这使您能够评估不同的目标方案，雷达要求和跟踪器配置，而无需访问昂贵的飞机或设备。此示例涵盖了整个合成数据工作流程。

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.

Define the tracking architecture of a system-of-systems that includes multiple detection-level multi-object trackers and track-level fusion algorithms. You can use the tracking architectures to compare different tracking system designs and find the best solution for your system.

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.

使用四个传感器发射器对模拟仅Bistatic范围检测。此外，此示例演示了如何使用仅Bistatic范围测量值定位和跟踪多个目标。

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.

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.

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.

Define architectures for a tracking system-of-systems in MATLAB and export them to a Simulink model. You compare various tracking system designs that includes multiple detection-level multi-object trackers and track fusers in Simulink. You use Simulink Variant systems to realize different architecture solutions for your system. This example closely follows the Define and Test Tracking Architectures for System-of-Systems MATLAB example.

雷达通用电气nerally 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.