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首页> 外文期刊>IEEE Transactions on Aerospace and Electronic Systems >Detection of a signal in linear subspace with bounded mismatch
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Detection of a signal in linear subspace with bounded mismatch

机译：在有界不匹配的线性子空间中检测信号

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摘要

We consider the problem of detecting a signal of interest in a background of noise with unknown covariance matrix, taking into account a possible mismatch between the actual steering vector and the presumed one. We assume that the former belongs to a known linear subspace, up to a fraction of its energy. When the subspace of interest consists of the presumed steering vector, this amounts to assuming that the angle between the actual steering vector and the presumed steering vector is upper bounded. Within this framework, we derive the generalized likelihood ratio test (GLRT). We show that it involves solving a minimization problem with the constraint that the signal of interest lies inside a cone. We present a computationally efficient algorithm to find the maximum likelihood estimator (MLE) based on the Lagrange multiplier technique. Numerical simulations illustrate the performance and the robustness of this new detector, and compare it with the adaptive coherence estimator which assumes that the steering vector lies entirely in a subspace
机译：我们考虑到在未知协方差矩阵的噪声背景下检测感兴趣信号的问题，并考虑了实际转向矢量与假定转向矢量之间可能的失配。我们假设前者属于一个已知的线性子空间，最多占其能量的一小部分。当感兴趣的子空间由推定的转向矢量组成时，这等于假设实际转向矢量和推定的转向矢量之间的角度为上限。在此框架内，我们得出了广义似然比检验（GLRT）。我们表明，它涉及解决最小化问题，且目标信号位于圆锥体内。我们提出一种计算有效的算法，以基于拉格朗日乘数技术找到最大似然估计器（MLE）。数值模拟说明了该新型检测器的性能和鲁棒性，并将其与自适应相干估计器进行了比较，后者假设转向矢量完全位于子空间中

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