Bayesian-MCMC-based parameter estimation of stealth aircraft RCS models  

作　　者：夏威 代小霞 冯圆 

机构地区：[1]School of Electronic Engineering, University of Electronic Science and Technology of China [2]Department of Electrical and Computer Engineering, Stevens Institute of Technology [3]China Academy of Electronics and Information Technology

出　　处：《Chinese Physics B》2015年第12期616-622,共7页中国物理B（英文版）

基　　金：Project supported by the National Natural Science Foundation of China(Grant No.61101173);the National Basic Research Program of China(Grant No.613206);the National High Technology Research and Development Program of China(Grant No.2012AA01A308);the State Scholarship Fund by the China Scholarship Council(CSC),and the Oversea Academic Training Funds,and University of Electronic Science and Technology of China(UESTC)

摘　　要：When modeling a stealth aircraft with low RCS（Radar Cross Section）, conventional parameter estimation methods may cause a deviation from the actual distribution, owing to the fact that the characteristic parameters are estimated via directly calculating the statistics of RCS. The Bayesian–Markov Chain Monte Carlo（Bayesian-MCMC） method is introduced herein to estimate the parameters so as to improve the fitting accuracies of fluctuation models. The parameter estimations of the lognormal and the Legendre polynomial models are reformulated in the Bayesian framework. The MCMC algorithm is then adopted to calculate the parameter estimates. Numerical results show that the distribution curves obtained by the proposed method exhibit improved consistence with the actual ones, compared with those fitted by the conventional method. The fitting accuracy could be improved by no less than 25% for both fluctuation models, which implies that the Bayesian-MCMC method might be a good candidate among the optimal parameter estimation methods for stealth aircraft RCS models.When modeling a stealth aircraft with low RCS（Radar Cross Section）, conventional parameter estimation methods may cause a deviation from the actual distribution, owing to the fact that the characteristic parameters are estimated via directly calculating the statistics of RCS. The Bayesian–Markov Chain Monte Carlo（Bayesian-MCMC） method is introduced herein to estimate the parameters so as to improve the fitting accuracies of fluctuation models. The parameter estimations of the lognormal and the Legendre polynomial models are reformulated in the Bayesian framework. The MCMC algorithm is then adopted to calculate the parameter estimates. Numerical results show that the distribution curves obtained by the proposed method exhibit improved consistence with the actual ones, compared with those fitted by the conventional method. The fitting accuracy could be improved by no less than 25% for both fluctuation models, which implies that the Bayesian-MCMC method might be a good candidate among the optimal parameter estimation methods for stealth aircraft RCS models.

关 键 词：stealth aircraft radar cross section fluctuation model Bayesian–Markov Chain Monte Carlo 

分 类 号：V218[航空宇航科学与技术—航空宇航推进理论与工程] V221