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Airborne/spacebased radar STAP using a structured covariance matrix

机译：使用结构化协方差矩阵的机载/空基雷达STAP

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摘要

It is shown that partial information about the airborne/spacebased (A/S) clutter covariance matrix (CCM) can be used effectively to significantly enhance the convergence performance of a block-processed space/time adaptive processor (STAP) in a clutter and jamming environment. The partial knowledge of the CCM is based upon the simplified general clutter model (GCM) which has been developed by the airborne radar community. A priori knowledge of parameters which should be readily measurable (but not necessarily accurate) by the radar platform associated with this model is assumed. The GCM generates an assumed CCM. The assumed CCM along with exact knowledge of the thermal noise covariance matrix is used to form a maximum likelihood estimate (MLE) of the unknown interference covariance matrix which is used by the STAP. The new algorithm that employs the a priori clutter and thermal noise covariance information is evaluated using two clutter models: 1) a mismatched GCM, and 2) the high-fidelity Research Laboratory STAP clutter model. For both clutter models, the new algorithm performed significantly better (i.e., converged faster) than the sample matrix inversion (SMI) and fast maximum likelihood (FML) STAP algorithms, the latter of which uses only information about the thermal noise covariance matrix.
机译：结果表明，有关机载/空基（A / S）杂波协方差矩阵（CCM）的部分信息可以有效地用于显着增强块处理时空自适应处理器（STAP）在杂波和干扰下的收敛性能。环境。 CCM的部分知识是基于机载雷达界开发的简化的通用杂波模型（GCM）。假定参数的先验知识应该可以由与此模型相关的雷达平台轻松测量（但不一定准确）。 GCM生成一个假定的CCM。假定的CCM与热噪声协方差矩阵的确切知识一起用于形成STAP使用的未知干扰协方差矩阵的最大似然估计（MLE）。使用两个杂波模型评估采用先验杂波和热噪声协方差信息的新算法：1）不匹配的GCM，以及2）高保真度研究实验室STAP杂波模型。对于这两种杂波模型，新算法的性能都比样本矩阵求逆（SMI）和快速最大似然（FML）STAP算法好得多（即收敛速度更快），后者仅使用有关热噪声协方差矩阵的信息。

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