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首页> 外文期刊>IEEE Geoscience and Remote Sensing Letters >Covariance Matrix Whitening-Based Training Sample Selection Method for Airborne Radar
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Covariance Matrix Whitening-Based Training Sample Selection Method for Airborne Radar

机译：基于协方差矩阵的空气雷达训练样本选择方法

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摘要

As training samples are not always target-free in space-time processing for airborne radar, the traditional methods usually use the sample covariance matrix (SCM) as the test covariance matrix (TCM) to censor contaminated training samples. However, the SCM cannot represent the property of the cell under test (CUT) accurately, resulting in low selection efficiency. To deal with this problem, this letter proposes a novel training sample selection method based on covariance matrix whitening. Specifically, we utilize the reconstructed subaperture's clutter covariance matrix (RSCCM) of the CUT as the TCM. The RSCCM is only determined by the CUT and can characterize the CUT directly. Then, we use the RSCCM to whiten the subaperture's covariance matrix of the training sample. A criterion for selecting the training samples is derived based on the maximum eigenvalue of the whitened subaperture's covariance matrix, which is related to the energy of the outliers and more stable than the statistic of the generalized inner product method. Simulations are conducted to evaluate the performance of the proposed method.
机译：由于培训样本并不总是在空中雷达时空处理中无目标，传统方法通常使用样品协方差矩阵（SCM）作为测试协方差矩阵（TCM），以审查受污染污染的训练样本。然而，SCM不能准确地代表被测电池（切割）的特性，从而产生低选择效率。要处理这个问题，这封信提出了一种基于协方差矩阵美白的新型培训样本选择方法。具体地，我们利用切割的重建的子孔节的杂波协方差矩阵（RSCCM）作为TCM。 RSCCM仅由切割确定，可以直接表征切割。然后，我们使用RSCCM来美白训练样本的子孔节的协方差矩阵。基于白细胞增长的协方差矩阵的最大特征值来导出用于选择训练样本的标准，这与异常值的能量和比广义内部产品方法的统计数据更稳定的能量有关。进行仿真以评估所提出的方法的性能。

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