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 ...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.展开更多
In this paper, the drawbacks of conventional target fluctuation models used in radar target modeling are set out. It is usually difficult to statistically model a real target because there are very few parameters whic...In this paper, the drawbacks of conventional target fluctuation models used in radar target modeling are set out. It is usually difficult to statistically model a real target because there are very few parameters which can be used to approximate the probability density function (PDF) of a real target's radar cross section (RCS) in conventional target models. A new method of statistical modeling is suggested, according to which the first nth central moment of real target's RCS, combined with the Legendre orthogonal polynomials, is used to reconstruct the PDF of the target's RCS. The relationship between the coefficients of the Legendre polynomials and the central moments of RCS are deduced mathematically. Through a practical computing example, the error-of-fit is shown as a function of the orders of Legendre coefficients. By comparing the errors-of-fit caused by both the new model and the conventional models, it is concluded that the new nonparametric method for statistical modeling of radar targets is superior.展开更多
Displacement control algorithms commonly used to evaluate axial force-bending moment(PM)diagrams may lead to incorrect interpretations of the strength envelopes for asymmetric sections.This paper aims to offer valuabl...Displacement control algorithms commonly used to evaluate axial force-bending moment(PM)diagrams may lead to incorrect interpretations of the strength envelopes for asymmetric sections.This paper aims to offer valuable insights by comparing existing displacement control algorithms with a newly proposed force control algorithm.The main focus is on the PM diagrams of three example sections that exhibit varying degrees of asymmetry.The comparative study indicates that conventional displacement control algorithms inevitably introduce non-zero out-of-plane bending moments.The reported PM diagram in such cases erroneously neglects the out-of-plane moment and fails to represent the strength envelope accurately.This oversight results in significant and unconservative errors when verifying the strength of asymmetric sections.展开更多
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)+1 种基金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)
文摘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.
文摘In this paper, the drawbacks of conventional target fluctuation models used in radar target modeling are set out. It is usually difficult to statistically model a real target because there are very few parameters which can be used to approximate the probability density function (PDF) of a real target's radar cross section (RCS) in conventional target models. A new method of statistical modeling is suggested, according to which the first nth central moment of real target's RCS, combined with the Legendre orthogonal polynomials, is used to reconstruct the PDF of the target's RCS. The relationship between the coefficients of the Legendre polynomials and the central moments of RCS are deduced mathematically. Through a practical computing example, the error-of-fit is shown as a function of the orders of Legendre coefficients. By comparing the errors-of-fit caused by both the new model and the conventional models, it is concluded that the new nonparametric method for statistical modeling of radar targets is superior.
基金supported by the Natural Science Foundation of China(52122811).
文摘Displacement control algorithms commonly used to evaluate axial force-bending moment(PM)diagrams may lead to incorrect interpretations of the strength envelopes for asymmetric sections.This paper aims to offer valuable insights by comparing existing displacement control algorithms with a newly proposed force control algorithm.The main focus is on the PM diagrams of three example sections that exhibit varying degrees of asymmetry.The comparative study indicates that conventional displacement control algorithms inevitably introduce non-zero out-of-plane bending moments.The reported PM diagram in such cases erroneously neglects the out-of-plane moment and fails to represent the strength envelope accurately.This oversight results in significant and unconservative errors when verifying the strength of asymmetric sections.