Matched Filtering on Directed Graphs
将匹配滤波原理从无向图扩展到有向图，处理非对称邻接矩阵和非正交特征向量，并通过数值例子验证。
The matched filter is a crucial concept in both signal analysis and convolutional neural networks (CNNs). Previous work has addressed graph matched filtering principles for undirected graphs. This article expands upon the existing literature, by exploring matched filtering principles for signals on directed graphs. In such cases, the adjacency matrix is asymmetric, and commonly results in nonorthogonal eigenvectors. The presented concept is supported by a detailed analysis and numerical examples.