Open AccessSingle-station Passive Reconnaissance Target Location Based on Intelligent Technology
Author(s) -
Kun He,
Xubo Liu,
Kaifeng Guo,
ChunYan Lan,
Yi Liu
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1544/1/012049
Subject(s) - position (finance) , computer science , power consumption , real time computing , artificial intelligence , sample (material) , base station , computer vision , power (physics) , telecommunications , chemistry , physics , finance , chromatography , quantum mechanics , economics
At present, passive reconnaissance target location usually adopts multi-station direction-finding cross or multi-station time difference method, which has the disadvantages of complex scenes, large resource consumption, long time consumption and low positioning accuracy. In this paper, intelligence technology is used to perform machine learning and predictive evaluation of target radiated power in battlefields, and combined with the electromagnetic propagation model to complete the estimation of target approximate position of single-station passive reconnaissance. The results show that after training with a certain sample, the method can quickly complete the estimation of the target position, and has high positioning accuracy.