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首页> 外文期刊>IEEE Transactions on Aerospace and Electronic Systems >Outlier resistant adaptive matched filtering
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Outlier resistant adaptive matched filtering

机译：抗异常值的自适应匹配滤波

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摘要

Robust adaptive matched filtering (AMF) whereby outlier data vectors are censored from the covariance matrix estimate is considered in a maximum likelihood estimation (MLE) setting. It is known that outlier data vectors whose steering vector is highly correlated with the desired steering vector, can significantly degrade the performance of AMF algorithms such as sample matrix inversion (SMI) or fast maximum likelihood (FML). Four new algorithms that censor outliers are presented which are derived via approximation to the MLE solution. Two algorithms each are related to using the SMI or the FML to estimate the unknown underlying covariance matrix. Results are presented using computer simulations which demonstrate the relative effectiveness of the four algorithms versus each other and also versus the SMI and FML algorithms in the presence of outliers and no outliers. It is shown that one of the censoring algorithms, called the reiterative censored fast maximum likelihood (CFML) technique is significantly superior to the other three censoring methods in stressful outlier scenarios.
机译：在最大似然估计（MLE）设置中考虑了健壮的自适应匹配滤波（AMF），从而从协方差矩阵估计中检查了异常数据。众所周知，其导向向量与所需导向向量高度相关的异常数据向量会大大降低AMF算法的性能，例如样本矩阵求逆（SMI）或快速最大似然（FML）。提出了四种检查异常值的新算法，这些算法是通过近似MLE解决方案得出的。两种算法均与使用SMI或FML估算未知的基础协方差矩阵有关。使用计算机仿真来显示结果，该仿真证明了在存在异常值和没有异常值的情况下，四种算法相对于彼此以及相对于SMI和FML算法的相对有效性。结果表明，在压力异常的情况下，一种称为迭代递减快速最大似然（CFML）的审查算法明显优于其他三种审查方法。

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