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PERFORMANCE EVALUATION OF TWO ARRAY FACTOR SYNTHESIS TECHNIQUES FOR STEERABLE LINEAR ARRAYS

机译:可控线性阵列的两种阵列因子综合技术的性能评估

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In this paper, a stochastic search method, the Differential Evolution algorithm and an analytical method, the Gauss Newton method are applied to synthesize a linear array factor focused in sidelobe level. Differential Evolution has proven to be an efficient method for real valued problems. On the other hand, Gauss Newton method has been applied as a generalization for error-based methods which use desired arrays factors. Due to physical considerations, a uniform amplitude excitation of elements is analyzed. For comparison purposes the results obtained are evaluated scanning the main beam over an angular sector to estimate the changes in parameters such as sidelobe level and half power beam width (HPBW). Preliminary results show a promising performance of Differential Evolution, improving the Gauss Newton method. Computational experiments are provided and discussed.
机译:本文采用随机搜索法,差分演化算法和解析方法高斯牛顿法合成侧瓣水平的线性阵列因子。事实证明,差异进化是解决实际问题的有效方法。另一方面,高斯牛顿法已被应用为基于误差的方法的一般化方法,该方法使用了所需的数组因子。出于物理考虑,对元素的均匀振幅激励进行了分析。为了进行比较,对获得的结果进行评估,方法是在一个角度扇区上扫描主波束,以估计参数的变化,例如旁瓣电平和半功率波束宽度(HPBW)。初步结果表明,差分进化法的性能令人鼓舞,改进了高斯牛顿法。提供并讨论了计算实验。

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