EMD Empirical Mode Decomposition

The study addresses the critical task of distinguishing UAVs from birds using mmWave MIMO radar under conditions where optical sensing is unreliable .

Micro-Doppler signatures generated by wing flapping and propeller rotation provide discriminative phase information in complex I/Q radar signals .

A region-of-interest is selected via the mean resultant length to isolate coherent micro-motion components .

The radar echoes are decomposed by Complex Empirical Mode Decomposition (CEMD) to obtain intrinsic mode function pairs .

Features include instantaneous frequency, bandwidth, total energy, and spectral entropy of IMFs .

A classification pipeline using PCA and RBF-SVM is trained on controlled anechoic-chamber data .

The method achieves about 95% accuracy across cross-validation and random train–test splits .

Permutation analysis shows high-frequency IMFs and spectral entropy are most informative .

Robustness is validated under additive complex Gaussian noise using EEMD variants .

The framework offers a scalable basis for real-time counter-UAS classification with future work on field deployment .