利用先验信息的幅度-相位联合STAP样本选取方法

Knowledge-Aided Amplitude and Phase Combined Training Strategy for Space-Time Adaptive Processing

针对分立杂波和强目标信号等引起的机载雷达杂波非均匀特性,提出一种利用先验信息的幅度-相位联合选取样本的方法,利用杂波多普勒频率与入射角度之间的先验关系,将检测多普勒通道在特定角度的幅度最强的单元作为样本集,确保对分立杂波的对消深度,再通过相位检测方法剔除样本集中相位明显偏离相位期望的样本,克服样本集包含目标信号引起的目标自相消效应。通过某机载相控阵雷达的实测数据比较研究表明,该方法较传统样本选取方法更具优势,且计算量较小便于工程采用。

A knowledge-aided amplitude and phase combined space-time adaptive processing （STAP） weight training strategy is presented to combat the main reasons of clutter inhomogeneous,clutter discrete and strong targets.According to the priori knowledge of clutter Doppler and angle of incidence,the method uses the range gates that have strongest power in the certain angle to form the training set ensuring the null depth for clutter discrete,and excludes the potential STAP training sample from training set if the measured phase significantly differs from the expected phase（angle） of clutter for the given Doppler,which ameliorates the target self-cancellation and clutter mitigation performance degradation when target signals are presented in STAP weight training set.The resulting training method based on both amplitude and phase selection criteria is shown to offer significant performance gains on experimental data.