基于改进PSO的分布式信号合成功率分配方法

Power Allocation Method for Distributed Signal Synthesis Based on Improved PSO

摘要:

针对传统电子对抗系统易被侦察定位，辐射功率有限等问题，提出一种改进的粒子群算法对分布式布阵的各结点功率进行优化；首先建立在地面半圆环式分散布阵，空中合成的数学模型及以目标合成点场强值为目标函数的有约束非线性规划问题模型；针对标准PSO在处理此类问题时收敛速度慢的缺陷提出一种基于Bernoulli混沌映射的种群初始化方法来增强初始种群的多样性；针对标准PSO在迭代中后期局部搜索能力差，易陷入局部最优等缺陷提出一种基于种群分离度与Tanh函数结合的自适应惯性权重设计方案；同时提出一种基于柯西分布的扰动策略以提高算法收敛精度；仿真实验结果表明改进的粒子群算法拥有比标准智能优化算法更高的收敛速度和寻优精度，经优化后的功率分配方案可有效增强信号合成效果。

Abstract:

The traditional electronic countermeasure systems have issues such as easy detection and location, limited radiation power, etc. To address these problems, an improved Particle Swarm Optimization (PSO) algorithm is proposed to optimize the power distribution of each node in a distributed array. First, a mathematical model of a semicircular ground-based dispersed array and airborne synthesis is established, along with a constrained nonlinear programming model with the target synthetic point field strength as the objective function. To overcome the slow convergence speed of the standard PSO when handling such problems, a Bernoulli chaotic mapping-based population initialization method is proposed to enhance the diversity of the initial population. To address the poor local search capability and susceptibility to local optima of the standard PSO in the middle and late iterations, an adaptive inertia weight design scheme combining population diversity and the Tanh function is proposed. Additionally, a Cauchy distribution-based perturbation strategy is introduced to improve the algorithm"s convergence accuracy. The simulation results show that the improved PSO algorithm has higher convergence speed and optimization accuracy than standard intelligent optimization algorithms, and the optimized power allocation scheme can effectively enhance the signal synthesis effect.