Design Of Electrostatic Signal Acquisition And Realize Target Recognition Method Based On LabVIEW

Passive electrostatic detection technology gets the related information by detecting the electrostatic field of the space around the moving target in the air. It has the advantages of simple structure,strong disguise and anti-stealth. In the future war, It will meet the needs, which warned low altitude penetration goal early and detected aircraft stealth for our army. This paper carry out research include the passive electrostatic detected principle, electrostatic target signal acquisition and typical target recognition method based on BP Neural Network.Firstly, this paper deduced electrostatic characteristics of Short-circuit axial current type based on analysing the charge principle of the moving objectives in the air and analyzed its target characteristic curves. Due to differences by target charge Qo, the speed of arget and detector passing vx and vertical dimensiony between the target and the detector y, we proposed a method of target recognition by state space equation. It will provide a theoretical basis for follow-up researchment.Secondly, this paper designed the acquisition system of electrostatic detection signal. The hardware part of the system mainly includes the detected electrode, signal processing circuit and data acquisition card. The data acquisition program is mainly composed of user login, signal acquisition program and data preservation. The electrostatic signal was collected by the simulatied platform of missile and target intersection. The results show that the error ranges of acquisition signal and theoretical simulated signal are allowable, so the design of the electrostatic acquisition system with high accuracy and reliability.Finally, the paper identify the range of typical targets by recognizing space equation of the electrostatic target characteristics. The BP neural network is used to train the sample data and the test results show that it can effective to identify the target. The diagnosed and recognized target data system of the BP neural network is designed on the Lab VIEW platform. Compared with the traditional method, this method can be simple and effective to the recognition results, which greatly reduces the difficulty in the diagnosis of the target data,, so it has a certain practical significance.