Open AccessA Horizontal Tracking Algorithm Suitable for Airborne Collision Avoidance System
Author(s) -
Yuchen Wang,
Liangfu Peng
Publication year - 2021
Publication title -
frontiers in signal processing
Language(s) - English
Resource type - Journals
eISSN - 2521-7380
pISSN - 2521-7372
DOI - 10.22606/fsp.2021.52001
Subject(s) - tracking (education) , kalman filter , computer science , cartesian coordinate system , track (disk drive) , noise (video) , extended kalman filter , filter (signal processing) , control theory (sociology) , computer vision , algorithm , artificial intelligence , mathematics , image (mathematics) , psychology , pedagogy , geometry , control (management) , operating system
The horizontal tracker is essential for the reliable operation of the Airborne Collision Avoidance System (ACAS). In ACAS target tracking, for the non-linear state estimation problem of using sensor measurement values to track in Cartesian coordinates, this paper proposes a horizontal tracking algorithm based on the Augmented Unscented Kalman Filter (AUKF) to achieve the horizontal of the target. Accurate tracking of direction. Firstly, the tracking model of the relative horizontal state of the target aircraft is established, and then the data is processed by AUKF. In order to verify the effectiveness of the horizontal tracking algorithm, the computer simulation method is used to simulate the track of the local aircraft and the intruder in the horizontal direction, and noise is added to the measured values. The traditional Kalman Filter In Polar Coordinates (KFPC) and AUKF algorithm are used to filter the ACAS horizontal tracking. The simulation results show that AUKF can achieve more accurate target tracking.