基于DE-GA算法的阵列天线故障检测方法
Array antenna fault detection method based on DE-GA algorithm
南敬昌 1陈鑫 1严洁1
作者信息
- 1. 辽宁工程技术大学电子与信息工程学院 葫芦岛 125105;辽宁省无线射频大数据智能应用重点实验室 葫芦岛 125105
- 折叠
摘要
为提高阵列天线故障检测的精度,提出了一种改进差分-遗传(DE-GA)算法.该算法融合了遗传(GA)算法和差分进化(DE)算法,在基因遗传过程中采取染色体双交叉策略,对陷入局部陷阱的个体信息进行重新引导;利用自适应权重优化后代的选择过程,提高算法对故障因子的灵敏性和适应能力.本文将该算法用于阵列天线的故障检测中,通过阵列公式建立天线的模型,对该模型的辐射方向图进行优化,使其与故障天线的已知辐射方向图逐渐拟合,以此推出故障阵列幅值.实验表明,本文提出的DE-GA算法与DE算法、GA算法相比,适应度函数值最低点分别减小了 11.15%和 12.90%,平均绝对误差分别减小了19.36%和 23.85%,均方误差分别减小了 12.90%和 11.15%,最大误差分别减小了 12.30%和 13.18%,具有更高的准确率,拟合能力更强.此外,在原有实验的基础上改变阵列的数量,该算法依然具有优良的稳定性,证明能够满足对大数量阵列的故障检测.
Abstract
To improve the accuracy of fault detection in array antenna,an enhanced differential evolution-genetic algorithm(DE-GA)is proposed.This algorithm combines the advantages of genetic algorithm(GA)and differential evolution(DE)by employing a dual crossover strategy to help individuals escape local optima.An adaptive weighting mechanism further optimizes offspring selection,enhancing the algorithm's sensitivity and adaptability to fault conditions.Applied to array antenna fault detection,the DE-GA algorithm models the array and optimizes its radiation pattern to match the known faulty pattern,allowing the faulty array's amplitude to be estimated.Experiments show that compared with DE and GA,DE-GA reduces the fitness function value by 11.15%and 12.90%,the mean absolute error by 19.36%and 23.85%,the mean square error by 12.90%and 11.15%,and the maximum error by 12.30%and 13.18%.This demonstrates higher accuracy and improved approximation capabilities.Additionally,the algorithm maintains excellent stability with larger arrays,making it suitable for large-scale fault detection.
关键词
阵列天线/故障检测/DE-GA算法/双交叉策略/自适应权重Key words
array antenna/fault detection/DE-GA algorithm/dual crossover strategy/adaptive weighting引用本文复制引用
出版年
2024
NETL
NSTL
万方数据