트랙 융합
아래의 예제에서는 트랙 간 융합기를 사용하여 여러 추적 소스에서 얻은 트랙을 융합합니다.
추천 예제
Lidar and Radar Fusion in Urban Air Mobility Scenario
Use multiobject trackers to track various unmanned aerial vehicles in an urban environment.
Track-to-Track Fusion for Automotive Safety Applications
Fuse tracks from two vehicles to provide a more comprehensive estimate of the environment than can be seen by each vehicle. The example demonstrates the use of a track-level fuser and the object track data format. In this example, you use the driving scenario and vision detection generator from Automated Driving Toolbox™, the radar data generator from the Radar Toolbox™, and the tracking and track fusion models from Sensor Fusion and Tracking Toolbox™.
레이다 데이터 및 라이다 데이터의 트랙에서의 수준 융합
이 예제에서는 레이다 센서와 라이다 센서의 측정값에서 객체 수준 트랙 목록을 생성하고 트랙 수준에서의 융합 방식을 사용하여 이들을 더욱더 융합하는 방법을 보여줍니다. 확장 객체 추적기를 사용하여 레이다 측정값을 처리하고 JPDA(Joint Probabilistic Data Association) 추적기를 사용하여 라이다 측정값을 처리합니다. 트랙 수준에서의 융합 방식을 사용하여 이러한 트랙을 더욱더 융합할 수 있습니다. 워크플로 구성도가 아래에 나와 있습니다.
Track-to-Track Fusion for Automotive Safety Applications in Simulink
Perform track-to-track fusion in Simulink® with Sensor Fusion and Tracking Toolbox™. In the context of autonomous driving, the example illustrates how to build a decentralized tracking architecture using a Track-To-Track Fuser block. In the example, each vehicle performs tracking independently as well as fuses tracking information received from other vehicles. This example closely follows the Track-to-Track Fusion for Automotive Safety Applications MATLAB® example.
Track-Level Fusion of Radar and Lidar Data in Simulink
Autonomous systems require precise estimation of their surroundings to support decision making, planning, and control. High-resolution sensors such as radar and lidar are frequently used in autonomous systems to assist in estimation of the surroundings. These sensors generally output tracks. Outputting tracks instead of detections and fusing the tracks together in a decentralized manner provide several benefits, including low false alarm rates, higher target estimation accuracy, a low bandwidth requirement, and low computational costs. This example shows you how to track objects from measurements of a radar and a lidar sensor and how to fuse them using a track-level fusion scheme in Simulink®. You process the radar measurements using a Gaussian Mixture Probability Hypothesis Density (GM-PHD) tracker and the lidar measurements using a Joint Probabilistic Data Association (JPDA) tracker. You further fuse these tracks using a track-level fusion scheme. The example closely follows the 레이다 데이터 및 라이다 데이터의 트랙에서의 수준 융합 MATLAB® example.
