Asynchronous Information Fusion in Intelligent Driving Systems for Target Tracking Using Cameras and Radars
Xiaohui Hao, Yuanqing Xia, Hongjiu Yang, Zhiqiang Zuo
Abstract
In this article, an asynchronous information fusion issue is investigated for a camera and a radar in an intelligent driving system. Local camera and radar estimators with missed detections are developed independently at synchronized state update time for target tracking. A Kuhn–Munkers algorithm is used to match local tracking results of camera and radar for fusion estimation of a same target. A fusion estimator is obtained by a matrix-weighted fusion algorithm with wide detection range and reliable fused estimates. Effectiveness of the proposed asynchronous fusion estimator is displayed by experimental results on road vehicle tracking.
Topics & Concepts
Computer visionSensor fusionAsynchronous communicationArtificial intelligenceEstimatorFusionComputer scienceRadar trackerRadarTracking (education)Tracking systemRange (aeronautics)EngineeringKalman filterMathematicsTelecommunicationsPedagogyPhilosophyStatisticsLinguisticsAerospace engineeringPsychologyTarget Tracking and Data Fusion in Sensor NetworksInfrared Target Detection MethodologiesDistributed Sensor Networks and Detection Algorithms