Networked Radar Anomaly Detection Based on Graph Deep Learning

Due to its stable performance advantages, networked radar has garnered increased attention in recent years. To address the numerous challenges of traditional radar anomaly detection, a graph deep learning-based method is proposed. This method models radar scenarios as graph structures and utilizes graph convolutional networks to learn and extract features from graph data, effectively capturing spatial and temporal information of anomalies. This approach significantly enhances detection accuracy and robustness. Experimental results demonstrate that the proposed algorithm achieves notable performance improvements in networked radar anomaly detection tasks, providing a viable solution for radar systems in complex environments.