作者: Gabriel Frenkel
DOI: 10.1117/12.217709
关键词: Filter (signal processing) 、 Missile defense 、 Radar 、 Engineering 、 Ballistic missile 、 Tracking (particle physics) 、 Kalman filter 、 Covariance matrix 、 Artificial intelligence 、 Computer vision 、 Sensor fusion
摘要: ABSTRACT This paper addresses the multisensor tracking of targets but considers only special case on a ballistic trajectory.The scenario consists two radars tr&king same target One these periodically sends track to other radarfor fusion with generated by recipient. A algorithm for is derived. Thisalgorithm exercised and illustrated Sensor Fusion Architecture Model (SFAM) computer program. Since therepeated ofballistic trajectories results in correlation that must be removed preserve optimality resul-taut estimate, an requires preservation last update error covariance matrix from anotherKalman Filter (1(F) also presented. These data then are used decorrelate inputs originating at same-source KF.Keywords: missile, Kalman (KF), radar, sensor fusion, 1. INTRODUCTION previous publication showed method collect combine measurements or more sensors toarrive accurate has far-reaching impact performance implementation Theater Missile Defense(TMD) system architectures. Although several candidate architectures feasible,2'3 one recurrent issues concerns