摘要
机会阵雷达大量天线单元在三维空间随机分布,波束综合远比二维平面稀疏阵复杂,为形成目标优化波束,需要约束多个自由度。提出了基于最小二乘-适应度评估的改进遗传算法用于三维机会阵波束综合优化,算法综合考虑了单元空间位置分布、激励状态、幅相权值等约束参数。提出将进化过程划分成若干"纪元",在每个"纪元"中采用最小二乘法拟合适应度变化曲线的新方法,该方法根据曲线斜率变化预测种群进化趋势,并据此自适应地改变遗传算子参数。结果表明:算法在提高优化效率的同时能有效避免传统方法容易出现的局部最优和过早收敛问题。
Pattern synthesis of 3-D opportunistic array radar (OAR) becomes more complex than traditional 2-D plane sparse array when a multitude of antennas are considered to be randomly distributed in a three dimensional space. In order to obtain an optimal pattern,several freedoms must be constrained. A new pattern synthesis approach based on the improved genetic algorithm using the least square fitness estimation method (LSFE-GA) is proposed for pattern synthesis optimization of 3-D opportunistic array radar. Para...
出处
《电波科学学报》
EI
CSCD
北大核心
2010年第1期93-98,共6页
Chinese Journal of Radio Science
关键词
机会阵雷达
波束综合
遗传算法
最小二乘-适应度评估
opportunistic array radar
pattern synthesis
genetic algorithm
the least square fitness estimation
