基于Hopfield神经网络算法的雷达干扰资源分配

Radar Jamming Resources Allocation Algorithm Based on Hopfield Neural Network

为了使有限的雷达干扰资源发挥最佳的干扰效果,基于干扰效益最大准则建立了雷达干扰资源优化分配模型。提出了基于Hopfield神经网络算法的干扰资源优化分配方法,利用该方法能够在可行的解空间中找出满足能量函数和约束条件的全局最优分配方案。并与干扰资源蚁群优化分配算法进行了比较,理论推导与仿真实验表明该方法的可行性与有效性,且比蚁群分配算法具有更快的收敛速度和更好的健壮性。

To get the best jamming effect by using limited radar jamming resources,the model for allocating radar jamming resource is built,which is based on maximum jamming effect. An new allocating radar jamming resource algorithm based on Hopfield Neural Network is presented, and it can find out the global optimal solution for radar jamming resources from feasible solution space,which satisfies the energy function and constraint condition. Algorithm experimental result is given in the .end, which shows that the new way has faster convergence rate and better stability, compared to ant colony algorithm.