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An application of hybrid differential evolution to 3-D near field source localization

机译:混合差分演化在3-D近场源定位中的应用

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3-D near field source localization is one of the hot areas of research which has found direct applications in Radar, Sonar and digital communication. In this work, we propose hybrid meta-heuristic based algorithm to estimate jointly and efficiently the range, elevation angle and amplitude of the near field sources impinging on uniform linear array. For this, first the Differential evolution (DE) and Interior Point Algorithm (IPA) are employed independently and then both of them are hybridized with each other to improve the accuracy and convergence rate further. In this hybridization, DE is used as a global optimization method while the IPA is acted as rapid local search optimizer. Mean Square Error is used as an objective evaluation function which requires single snapshot to converge and avoids any ambiguity among the angles that are supplement to each other. The proposed hybrid scheme (DE-IPA) produced better results as compare to DE and IPA alone. Moreover, the DE-IPA is also compared with the other hybrid meta-heuristic technique based on Genetic Algorithm hybridized with Interior point Algorithm (GA-IPA). The comparison is made on the basis of estimation accuracy, robustness against noise, convergence rate and root mean square error. The validity and effectiveness of the proposed hybrid scheme is exploited on the basis of large number of Monte-Carlo simulations.
机译:3-D近场源定位是研究的热点之一,已直接在雷达,声纳和数字通信中得到应用。在这项工作中,我们提出了一种基于混合元启发式算法的算法,可以联合并有效地估计撞击均匀线性阵列的近场源的范围,仰角和幅度。为此,首先独立使用差分演化(DE)和内部点算法(IPA),然后将两者相互混合以进一步提高准确性和收敛速度。在这种混合中,DE被用作全局优化方法,而IPA被用作快速本地搜索优化器。均方误差用作目标评估函数,该函数需要单个快照进行收敛,并且避免了彼此补充的角度之间的任何歧义。与单独的DE和IPA相比,提出的混合方案(DE-IPA)产生了更好的结果。此外,DE-IPA还与其他基于遗传算法与内部点算法(GA-IPA)混合的混合元启发式技术进行了比较。比较是基于估计精度,抗噪声能力,收敛速度和均方根误差进行的。提出的混合方案的有效性和有效性是在大量的蒙特卡洛模拟的基础上开发的。

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