报告题目：Adaptive Detection Exploiting Persymmetry in Multichannel Radars
报 告 人：刘军 副教授 中国科技大学信息学院
In the conventional adaptive detection algorithms for multichannel radars, a set of homogeneous training data is required for the clutter covariance matrix estimation to suppress clutter. In practice, the heterogeneity and insufficiency of the training data lead to significant performance losses in the conventional adaptive detection algorithms. In addition, the target steering vector mismatches obviously deteriorate the detection performance. In this presentation, the structure of clutter covariance matrix (i.e., persymmetry) is exploited to design novel adaptive detectors. Numerical examples demonstrate that detection performance gains can be achieved.