This letter proposes an enhanced matrix constant false alarm rate (CFAR) detection method that works on the lower-dimensional Riemannian manifold. Motivated by general matrix CFAR detection method and dimensionality reduction scheme of the Riemannian manifold, this method obtains a mapping by maximizing the geometric test statistic. Dimensionality reduction is formulated as an orthonormal constraint optimization problem on the Grassmann manifold. Moreover, an explicit mapping is obtained by solving the optimization problem via conjugate gradient approach. Performances of the proposed method are evaluated on the lower-dimensional Riemannian manifold. Experiments on simulated data and real sea clutter data demonstrate that our method leads to the robustness to outliers and the improvement of detection performance over classical methods.

中文翻译：

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使用黎曼流形降维的增强型矩阵 CFAR 检测

这封信提出了一种适用于低维黎曼流形的增强型矩阵常数误报率 (CFAR) 检测方法。该方法受通用矩阵CFAR检测方法和黎曼流形降维方案的启发，通过最大化几何检验统计量来获得映射。降维被表述为 Grassmann 流形上的正交约束优化问题。此外，通过共轭梯度法求解优化问题，获得了显式映射。在低维黎曼流形上评估了所提出方法的性能。模拟数据和真实海杂波数据的实验表明，我们的方法对异常值具有鲁棒性，并且检测性能优于经典方法。