TV Guidance Simulation Platform Based on Deep Learning

Hardware-in-the-Loop TV Guidance System Simulation Platform (HIL-GSS) is an experimental platform designed by the School of Astronautics, Beihang University, which is used for the hardware-in-the-loop simulation of TV guided missile-target engagement. The platform has been applied to research and ex...

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Bibliographic Details
Published in:IEEE ... International Conference on Cybernetics and Intelligent Systems (Print) pp. 89 - 94
Main Authors: Yu, Zhaowei, Chen, Wanchun, Chen, Zhongyuan, Liu, Xiaoming
Format: Conference Proceeding
Language:English
Published: IEEE 01.11.2019
Subjects:
Computational modeling
Hu moments
Image segmentation
Machine learning
Solid modeling
Target recognition
target simulation
Three-dimensional displays
TV guidance
Unity3D
YOLO
ISSN:2326-8239
Online Access:Get full text
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Summary:Hardware-in-the-Loop TV Guidance System Simulation Platform (HIL-GSS) is an experimental platform designed by the School of Astronautics, Beihang University, which is used for the hardware-in-the-loop simulation of TV guided missile-target engagement. The platform has been applied to research and experiment for many years and highly praised. However, the platform currently has the following problems: 1) Fidelity of the fixed-size target is not high enough; 2) The target recognition algorithm is only based on color feature; 3) The contour recognition algorithm based on Hu moments detects the target with slow speed and poor accuracy. In order to solve above problems, we tried to improve the contour recognition algorithm firstly. Secondly, we used 3D model to build the scene of target simulation with Unity 3D. Thirdly, we applied YOLO, a deep-learning-based target recognition algorithm, to achieve accurate recognition of targets in different attitudes. In the end, several experiments were carried out by using YOLO and color-based target recognition algorithm respectively to verify the feasibility of using YOLO for TV guidance simulation experiment.
ISSN:2326-8239
DOI:10.1109/CIS-RAM47153.2019.9095814