Matched Filtering on Directed Graphs

doi:10.1109/TSMC.2024.3407948

Matched Filtering on Directed Graphs

2024-09-01

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DOI：10.1109/TSMC.2024.3407948

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Abstract

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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.

Keywords:

Symmetric matrices

Directed graphs

Convolution

White noise

Spectral analysis

Transfer functions

Machine learning

Asymmetric adjacency matrices

convolutional neural network (CNN)

directed graphs

graph signal processing

matched filter

Matched Filtering on Directed Graphs

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