Radar cross section (RCS) reduction technologies are very important in survivability of the military naval vessels. Ship appearance shaping as an effective countermeasure of RCS reduction redirects the scattered energ...Radar cross section (RCS) reduction technologies are very important in survivability of the military naval vessels. Ship appearance shaping as an effective countermeasure of RCS reduction redirects the scattered energy from one angular region of interest in space to another region of little interest. To decrease the scattering electromagnetic signals from ship scientifically, optimization methods should be introduced in shaping design. Based on the assumption of the characteristic section design method, mathematical formulations for optimal shaping design were established. Because of the computation-intensive analysis and singularity in shaping optimization, the response surface method (RSM) combined genetic algorithm (GA) was proposed. The poly-nomial response surface method was adopted in model approximation. Then genetic algorithms were employed to solve the surrogate optimization problem. By comparison RCS of the conventional and the optimal design, the superiority and effectiveness of proposed design methodology were verified.展开更多
基金the National Natural Science Founda-tion of China (No. 10672100)
文摘Radar cross section (RCS) reduction technologies are very important in survivability of the military naval vessels. Ship appearance shaping as an effective countermeasure of RCS reduction redirects the scattered energy from one angular region of interest in space to another region of little interest. To decrease the scattering electromagnetic signals from ship scientifically, optimization methods should be introduced in shaping design. Based on the assumption of the characteristic section design method, mathematical formulations for optimal shaping design were established. Because of the computation-intensive analysis and singularity in shaping optimization, the response surface method (RSM) combined genetic algorithm (GA) was proposed. The poly-nomial response surface method was adopted in model approximation. Then genetic algorithms were employed to solve the surrogate optimization problem. By comparison RCS of the conventional and the optimal design, the superiority and effectiveness of proposed design methodology were verified.
