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Clutter covariance smoothing by subaperture averaging

机译:子孔径平均对杂波协方差平滑

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Space-time processing has been proposed as an approach by which an airborne radar can adaptively detect small targets in the presence of ground clutter and jammers, provided the covariance matrix of the interference is available. One of the difficulties in estimating the covariance matrix is the need to obtain a sufficient number of independent samples, especially for high pulse-repetition-frequency (PRF) systems. It is shown here that this difficulty can be overcome by combining subaperture averaging, or spatial smoothing, of the received data with conventional range bin averaging
机译:提出了时空处理作为一种方法,通过该方法,机载雷达可以在存在地面杂波和干扰的情况下自适应地检测小目标,前提是可以使用干扰的协方差矩阵。估计协方差矩阵的困难之一是需要获得足够数量的独立样本,尤其是对于高脉冲重复频率(PRF)系统。在此表明,可以通过将接收数据的子孔径平均或空间平滑与常规距离仓平均相结合来克服此困难

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