Ensemble of differential evolution algorithms for electromagnetic target recognition problem
Ensemble of differential evolution algorithms for electromagnetic target recognition problem
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DOI：
10.1049/iet-rsn.2012.0212
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发表时间：
2013-08
影响因子：
1.7
通讯作者：
M. Seçmen;M. Tasgetiren
中科院分区：
文献类型：
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作者：
M. Seçmen;M. Tasgetiren
提出了一种用于识别共振散射区电磁目标的微分进化集成算法。该算法的目的是为每个目标合成一个特殊的入射信号，该信号被定义为给定目标识别方法中的主要鉴别特征。在该算法中，对基函数的幅值和入射信号的持续时间进行了优化，以使后期散射信号的能量最小，这是该算法的主要适应度函数。将该算法应用于由无损介质球组成的目标集，获得了对无噪信号和有噪信号的正确识别率。文中还给出了改进的DE算法与传统DE的其他DE变体、带可选外部档案的自适应差分进化算法(JADE)、JDE算法的结果比较，表明了所提算法的有效性。
In this study, an ensemble of differential evolution (DE) algorithms is presented to classify electromagnetic targets in resonance scattering region. The algorithm aims to synthesize a special incident signal for each target, which is defined as the main discrimination feature in the given target recognition method. In the proposed algorithm, the amplitudes of basis functions and the duration of this incident signal are optimised to give minimum late-time scattered signal's energy, which is the main fitness function of the algorithm. The proposed DE algorithm is applied to a target set consisting of lossless dielectric spheres and correct recognition rates for both noiseless and noisy signals are obtained. The results for both developed DE algorithm and other DE variants of traditional DE, adaptive differential evolution with optional external archive (JADE), jDE are also given to compare the algorithms and show the effectiveness of the proposed one.