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Synthesizing broad null in linear array by amplitude-only control using wind driven optimization technique

机译:使用风动优化技术通过仅振幅控制来合成线性阵列中的宽零点

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This paper describes fast, efficient and global optimization method called wind driven optimization (WDO) algorithm for nulling pattern synthesis of uniformly spaced linear array having maximum side lobe level (SLL) suppression, restricted dynamic range ratio (DRR), beam width and null control by controlling the array elements amplitude-only. A broad null is placed in the direction of maximum interference by undesired signals while receiving signal from the desired direction. The WDO is a new nature-inspired evolutionary algorithm derived from to the point movement of the air parcel in the earth's atmosphere. It uses a new learning strategy to update the velocity and position of air packets based on their current pressure values to accelerate the convergence. The pressure (objective) function is based on an exact penalty method. The results are compared with those obtained by other evolutionary algorithm such as bacterial foraging optimization (BFO), plant growth simulation algorithm (PGSA) and bee algorithm. The simulation study demonstrates that the WDO outperforms the three algorithms particularly in terms of minimum SLL, beam width control, DRR, null control and the rate of convergence.
机译:本文介绍了一种称为风动优化(WDO)算法的快速,高效和全局优化方法,该方法用于对具有最大旁瓣电平(SLL)抑制,受限动态范围比(DRR),波束宽度和空值控制的均匀间隔线性阵列的空值模式进行合成通过控制阵列元素仅振幅。在从所需方向接收信号的同时,将不期望的信号置于最大干扰方向上。 WDO是一种新的,受自然启发的进化算法,它源自于空气在地球大气中的点移动。它使用一种新的学习策略,根据空气包的当前压力值来更新其速度和位置,以加快收敛速度​​。压力(目标)函数基于精确惩罚方法。将结果与通过其他进化算法(例如细菌觅食优化(BFO),植物生长模拟算法(PGSA)和蜂算法)获得的结果进行比较。仿真研究表明,WDO在最小SLL,波束宽度控制,DRR,空值控制和收敛速率方面优于三种算法。

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