反演蒸发波导的改进粒子群优化算法

An Improved Particle Swarm Optimization Algorithm for Inversion of Evaporation Ducts

雷达利用携带的海杂波信息可以反演出海面蒸发波导参数。为了提高蒸发波导反演性能,提出了一种改进的粒子群优化算法。当实测雷达海杂波功率与蒸发波导模型计算所得杂波功率之间建立的目标函数取最小值时,可反演得到最接近实测蒸发波导剖面参数。根据这一思想,在基本粒子群算法基础上通过对惯性权重和学习因子进行自适应调整,引入自适应压缩因子来确保算法快速收敛,并获得高精度的蒸发波导参数。算法仿真实验证明,改进粒子群优化算法相比于基本粒子群算法具有较好的全局收敛性,在处理较大规模数据时反演速度明显提高。

The radar can invert the parameters of sea evaporator ducts by using the sea clutter information.In order to improve the performance of evaporative duct inversion, an improved Particle Swarm Optimization(PSO) algorithm is proposed.When taking the minimum value, the objective function established between the actually measured radar sea clutter power and the clutter power calculated by the evaporation duct model can reverse the profile parameters closest to the actually measured evaporation ducts.According to this idea, the inertia weights and learning factors are adaptively adjusted based on the basic PSO algorithm, and the adaptive compression factor is introduced to ensure the fast convergence of the algorithm, thus the high-precision evaporation duct parameters can be obtained.Simulation experiment proves that: compared with basic PSO algorithm, the improved PSO algorithm has better global convergence performance, and the inversion speed is obviously improved when dealing with large-scale data.