Application of PCA and Unsupervised Deep Learning in Bird and Drone Discrimination Based on FMCW Radar Measurements

In low-altitude airspace surveillance, distinguishing between birds and drones is crucial due to their overlapping radar signatures. Radar, the preferred technology for long-range surveillance, struggles with this differentiation. To address this, our study introduces an unsupervised deep-learning method utilizing real radar data from birds and UAVs. This approach starts with data cleaning and up-sampling using Synthetic Minority Over-sampling Technique (SMOTE) to manage dataset imbalance. We integrate Principal Component Analysis (PCA) with deep learning to reduce the feature set efficiently. This integration minimizes computational demands while retaining essential information for precise clustering, enhancing real-world applicability. A Deep Clustering Network (DCN) exploits the reduced-dimensional space created by PCA to identify distinct signal clusters for birds and drones, optimized for radar surveillance without relying on predefined labels. A deep neural network maps data into a cluster-friendly hidden space, designed for radar signal analysis. The model's effectiveness, with an average Normalized Mutual Information (NMI) score of 0.878 through K-fold cross-validation, underscores the innovative potential of combining PCA with unsupervised learning. This method overcomes traditional radar techniques' limitations, offering a scalable and efficient solution for surveillance scenarios.