Open AccessRadar/ESM anti‐bias track association algorithm based on track distance vector detection
Author(s) -
Li Baozhu,
Dong Yunlong,
Huang Gaodong,
Chen Xiaolong,
Guan Jian
Publication year - 2019
Publication title -
the journal of engineering
Language(s) - English
Resource type - Journals
ISSN - 2051-3305
DOI - 10.1049/joe.2019.0647
Subject(s) - track (disk drive) , algorithm , computer science , radar , azimuth , state vector , monte carlo method , association (psychology) , gaussian , radar tracker , artificial intelligence , mathematics , statistics , telecommunications , physics , philosophy , geometry , epistemology , classical mechanics , quantum mechanics , operating system
To address radar/ESM track association problem in the presence of sensor biases and different targets reported by different sensors, an anti‐bias track association algorithm based on track distance vectors detection is proposed according to the statistical characteristics of Gaussian random vectors. The state estimation decomposition equation is first derived in MPC. The track distance vectors are obtained by the real state cancellation method. Second, in order to eliminate most non‐homologous target tracks, the rough association is performed according to the features of the azimuthal rate and inverse‐time‐to‐go (ITG). Finally, the track‐to‐track association of radar and ESM is extracted based on track distance vectors chi‐square distribution. The effectiveness of the proposed algorithm are verified by Monte–Carlo simulation experiments in the presence of sensor biases, targets densities, and detection probabilities.