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Chin. Phys. B, 2015, Vol. 24(12): 129501    DOI: 10.1088/1674-1056/24/12/129501
GEOPHYSICS, ASTRONOMY, AND ASTROPHYSICS Prev  

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

Xia Wei (夏威)a b, Dai Xiao-Xia (代小霞)a c, Feng Yuan (冯圆)c
a School of Electronic Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China;
b Department of Electrical and Computer Engineering, Stevens Institute of Technology, Hoboken NJ 07030, USA;
c China Academy of Electronics and Information Technology, Beijing 100041, China
Abstract  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.
Keywords:  stealth aircraft      radar cross section      fluctuation model      Bayesian-Markov Chain Monte Carlo  
Received:  25 May 2015      Revised:  11 August 2015      Accepted manuscript online: 
PACS:  95.30.Jx (Radiative transfer; scattering)  
Fund: 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).
Corresponding Authors:  Xia Wei     E-mail:  wx@uestc.edu.cn

Cite this article: 

Xia Wei (夏威), Dai Xiao-Xia (代小霞), Feng Yuan (冯圆) Bayesian-MCMC-based parameter estimation of stealth aircraft RCS models 2015 Chin. Phys. B 24 129501

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