Fusion and Optimality of Fuze and Seeker Target Detection Information Based on The Neural Networks

Author

Wei-Daozhi ; He-Guangjun ; Wu-Jianfeng ; Li-Jiong

Author_Institution

Missile Inst., Air Force Eng. Univ., Xi´an

fYear

2009

fDate

28-29 April 2009

Firstpage

1

Lastpage

3

Abstract

Based on the complementarities of ground-to-air missile fuze and seeker target detection information, the feasibility of their fusion and optimality is analyzed. This paper proposes an multi-layer feed forward neural network OBP (optimal-back-propagation) algorithm, and structures a fusion and optimality model of target detection information. Simulation result shows the detection information can be controlled in the required range well through fusion and optimality, which can reach the requirements for the optimal delay time and the optimal detonation azimuth in high precision.

Keywords

backpropagation; feedforward neural nets; military computing; missiles; object detection; ground-to-air missile fuze; multilayer feedforward neural network; neural networks; optimal backpropagation algorithm; optimal detonation azimuth; seeker target detection information; Azimuth; Delay effects; Feedforward neural networks; Feeds; Information analysis; Missiles; Multi-layer neural network; Neural networks; Object detection; Optimal control;

fLanguage

English

Publisher

ieee

Conference_Titel

Testing and Diagnosis, 2009. ICTD 2009. IEEE Circuits and Systems International Conference on

Conference_Location

Chengdu

Print_ISBN

978-1-4244-2587-7

Type

conf

DOI

10.1109/CAS-ICTD.2009.4960827

Filename

4960827