Model identification of EMP electromagnetic coupling using Volterra kernel

Gao, Yongsheng; Fang, Liqing; Wei, Ming; Ma, Jia

doi:10.1504/ijmic.2011.041304

Cited by 2 publications

(1 citation statement)

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“…That is to say, it is feasible to identifying nonparametric model for indefinite complex electric system by using Volterra series. System identifying using Volterra kernel provide a new path to solve complex electromagnetic coupling modelling problems [5] [6].…”

Section: Introductionmentioning

confidence: 99%

Nonlinearity Mode Theory Foundation Research of EMP Coupling by Volterra Series

Gao¹,

He³

et al. 2014

AMM

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Strong Electromagnetic Pulses (EMP) as a new idea weapon has caused many effects like malfunctions, performance degradation, interferences, and destructions in electronic and electrical systems. EMP coupling model research is the foundation of effect evolution and protection. In this paper, a new modeling method on the base of combination system identification theory and Volterra series algorithm was utilized to analyze the electromagnetic coupling process from an external field to an inner electric system. First, we will analyze the theory foundation of non-parameters model evolution. And then, using input and output test data, we presented commonly Voleterra series expression of EMP electromagnetic coupling model. Then, we analyze coupling models several important characteristics in detail. Finally, we approach High Order Spectrum algorithms to identify Volterra kernel functions. Theoretical results show that the Volterra kernel function will be used as an efficient method in EMP coupling model research and can apply this method to character the nonlinear behavior of the EMP electromagnetic coupling. The model described above was assigned under certain assumptions, moreover describes the effects in theory only and does not consider the time variation of the parameters of the system. From this reason it appears as more convenient to characterize the electromagnetic coupling process, by a more universal mathematical approach.

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Section: Introductionmentioning

confidence: 99%