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A Novel Elliptical-Cylindrical Antenna Array for Radar Applications

机译:一种新型的雷达应用椭圆圆柱天线阵列

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With the advancement of radar technology, detecting objects, determining the structure of the target, and estimating the direction and the speed is prominently increasing. There is no doubt that small cross section targets are hardly identified and determined. This problem demands the need for antenna pattern synthesizing with a high gain and highly reduced sidelobe level (SLL). In this paper, an optimization technique called self-adaptive differential evolution (SaDE) that can be able to learn and behave intelligently is integrated to determine an optimal set of excitation weights in the design of elliptical–cylindrical antenna array (ECAA). In this work, the SaDE-optimized hyper beam is proposed to achieve qualified radar performances. Nonuniform excitation amplitudes of the individual array elements of ECAA are performed to obtain reduced SLL, high directivity, and flexible radiation pattern which are crucial in radar applications. With increased antenna gain, small cross section objects can be accurately identified and determined even in volatile environment where there are different natures of interferences. Besides, the proposed work enables steering electronically and it allows scanning very rapidly across a wide range of elevation and azimuth planes. To evaluate the improved performance of the proposed optimization scheme, comparison is done with genetic algorithm (GA), particle swarm optimization (PSO), biogeography-based optimization (BBO), and invasive weed optimization (IWO) against different parameters. In general, the proposed work of pattern synthesis has resulted in much better reduction in SLL and first null beamwidth (FNBW) than both the uniformly excited and thinned ECAA. The results of this study clearly revealed that the SLL is highly reduced at a very directive beamwidth. The proposed antenna array offers high system availability over wide range of radar applications.
机译:随着雷达技术的进步,检测物体,确定目标的结构以及估计方向和速度的趋势显着增加。毫无疑问,几乎没有确定和确定小的横截面目标。这个问题需要具有高增益和高度降低的旁瓣电平(SLL)的天线方向图合成。在本文中,集成了一种可以智能学习和表现的称为“自适应差分进化(SaDE)”的优化技术,以确定椭圆-圆柱天线阵列(ECAA)设计中的最佳激励权重集。在这项工作中,提出了经过SaDE优化的超光束,以实现合格的雷达性能。对ECAA的各个阵列元件执行非均匀的激发幅度,以获得降低的SLL,高方向性和灵活的辐射方向图,这在雷达应用中至关重要。随着天线增益的增加,即使在具有不同干扰性质的易变环境中,也可以准确地识别和确定小截面物体。此外,所提出的工作使电子转向成为可能,并且允许在很大范围的仰角和方位平面上非常快速地进行扫描。为了评估所提出优化方案的改进性能,将遗传算法(GA),粒子群优化(PSO),基于生物地理的优化(BBO)和侵入性杂草优化(IWO)针对不同参数进行了比较。通常,与均匀激发的ECAA和减薄的ECAA相比,模式合成的拟议工作已导致SLL和第一空波束宽度(FNBW)的降低要好得多。这项研究的结果清楚地表明,在非常定向的波束宽度下,SLL大大降低了。拟议的天线阵列可在各种雷达应用中提供高系统可用性。

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