辅助数据缺失环境下结构化自适应目标检测器

杂波背景非均匀导致经典自适应检测方法性能下降,为改善辅助数据缺失环境下的目标检测性能,研究了雷达目标结构化自适应检测方法。利用杂波协方差矩阵斜对称特征,设计了结构化广义似然比检验自适应检测器,提高了杂波谱结构化信息和待检测单元杂波信息的利用率;进一步推导了检测器的实数域表达式。仿真分析表明,所提检测器具有恒虚警率特性,其检测性能对不同杂波相关性具有很好的鲁棒性。在辅助数据缺失环境下,所提检测器性能优于现有典型非结构化和结构化检测器,且这种优势随着辅助数据缺失程度的增大而加大。

Adaptive structured detector of radar target with scarce secondary data

Non-homogeneous background clutter usually results in the performance degradation of traditional adaptive detectors. To improve the detection performance in scarce secondary data environment, an adaptive structured detector of radar targets is addressed herein. By utilizing the persymmetry of the clutter covariance matrix, an adaptive structured detector is designed based on the generalized likelihood ratio test, which enhances the information utilization of the structural character of clutter spectral properties and the clutter in the cell under test. In the sequel, a real domain version is derived. The experimental results show that, the proposed detector has the constant false alarm rate and it performs robustly for different correlations of clutter. It is shown that, the proposed detector outperforms the traditional structured and unstructured detectors in scarce secondary data environment; and this advantage augments as the number of secondary data approaches the available minimum limit.