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Target detection with linear and kernel subspaces matching in the presence of strong clutter

机译:目标检测用线性和内核子空间匹配在存在强杂乱的情况下

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This paper proposes potential approaches to detect the weak target in the presence of strong disturbance. The disturbance consists of strong clutter and white Gaussian noise. The target and clutter are assumed to lie in the corresponding subspaces. The algorithms of subspace matching in the linear and kernel subspaces are derived respectively. The leading eigenvector matching that is the subspace with rank one is investigated as well. The simulation is done for two sensor arrays based on the characteristics of the clutter environment. The results from the simulation show the potential and promising uses of the proposed algorithms to detect the weak target.
机译:本文提出了在强烈干扰存在下检测弱目标的潜在方法。 干扰包括强杂波和白色高斯噪音。 假设目标和杂波位于相应的子空间中。 分别导出了线性和内核子空间中的子空间匹配的算法。 领先的特征向量匹配,也是一个排名的子空间。 基于杂波环境的特性,为两个传感器阵列进行仿真。 仿真结果显示了所提出的算法的潜在和有希望的用途来检测弱目标。

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