论文标题
移动目标指示系统的自适应过滤器
Adaptive filters for the moving target indicator system
论文作者
论文摘要
自适应算法属于雷达目标检测中使用的一类重要算法，以克服干扰协方差的先前不确定性。通过有用的信号对经验协方差矩阵的污染导致这种类别的自适应算法的性能显着降解。正规化也可以在雷达文献中被称为样品协方差加载，可用于应对原始问题的不良调节和通过基于样品协方差矩阵反转的自适应算法的所需信号对经验协方差的污染。但是，除非对协方差矩阵的结构和有用的信号穿透模型做出了强有力的假设，否则无法得出加载因子的最佳值。同样，具有线性约束或没有约束的最小平方算法也对具有目标信号的学习样本污染也很敏感。我们合成了两种方法，以改善自适应算法的收敛性，并保护它们免受来自目标信号的污染。所提出的方法基于经验信号与干扰和噪声比（SINR）的最大化。使用模拟数据证明了其有效性。
Adaptive algorithms belong to an important class of algorithms used in radar target detection to overcome prior uncertainty of interference covariance. The contamination of the empirical covariance matrix by the useful signal leads to significant degradation of performance of this class of adaptive algorithms. Regularization, also known in radar literature as sample covariance loading, can be used to combat both ill conditioning of the original problem and contamination of the empirical covariance by the desired signal for the adaptive algorithms based on sample covariance matrix inversion. However, the optimum value of loading factor cannot be derived unless strong assumptions are made regarding the structure of covariance matrix and useful signal penetration model. Similarly, least mean square algorithm with linear constraint or without constraint, is also sensitive to the contamination of the learning sample with the target signal. We synthesize two approaches to improve the convergence of adaptive algorithms and protect them from the contamination of the learning sample with the signal from the target. The proposed approach is based on the maximization of empirical signal to interference plus noise ratio (SINR). Its effectiveness is demonstrated using simulated data.
