中国物理B ›› 2015, Vol. 24 ›› Issue (12): 129501-129501.doi: 10.1088/1674-1056/24/12/129501

• GEOPHYSICS, ASTRONOMY, AND ASTROPHYSICS • 上一篇    

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

夏威a b, 代小霞a c, 冯圆c   

  1. 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
  • 收稿日期:2015-05-25 修回日期:2015-08-11 出版日期:2015-12-05 发布日期:2015-12-05
  • 通讯作者: Xia Wei E-mail:wx@uestc.edu.cn
  • 基金资助:
    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).

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   

  1. 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
  • Received:2015-05-25 Revised:2015-08-11 Online:2015-12-05 Published:2015-12-05
  • Contact: Xia Wei E-mail:wx@uestc.edu.cn
  • Supported by:
    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).

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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.

关键词: stealth aircraft, radar cross section, fluctuation model, Bayesian-Markov Chain Monte Carlo

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.

Key words: stealth aircraft, radar cross section, fluctuation model, Bayesian-Markov Chain Monte Carlo

中图分类号:  (Radiative transfer; scattering)

  • 95.30.Jx