Feature-Level Robustness Analysis of Physics-Guided Micro-Doppler Descriptors for classification of Drones and Birds

While micro-Doppler signatures are a proven modality for discriminating between drones and birds, their reliability remains questionable in low-SNR, data-constrained environments where other methods such as deep learning models might struggle. This paper presents a systematic analysis of ten statistical and physics-motivated handcrafted features for micro-Doppler classification under controlled signal degradation, using a publicly available 77 GHz frequency modulated continuous wave (FMCW) radar dataset. Micro-Doppler spectrograms are corrupted using Additive White Gaussian Noise (AWGN), phase noise, and their combined effects across a range of −10 dB to 10 dB signal-to-noise ratios (SNRs) and 1°- 10° phase noise levels. Features extracted from the raw and corrupted spectrograms are evaluated via performance metrics within a 5-outer × 5-inner stratified cross-validation framework for a Support Vector Machine (SVM) and Random Forest classifier, with fixed hyperparameters across all noise levels chosen through grid search. Feature relevance under noise is understood through the lens of permutation based importance calculated as the decrease in macro-averaged F1 score induced by random shuffling of a single feature while keeping all other features fixed. For noise-free data mean classification accuracies of 0.916 ± 0.095 for the SVM and 0.916 ± 0.060 for the Random Forest classifier were achieved with F1 scores of 0.909 and 0.912. Experimental results reveal that entropy-based and side-lobe features maintain stable discriminative performance under severe noise, reporting macro-averaged F1 scores of 0.773 and 0.831 for the SVM and Random Forest, respectively. Feature-wise evaluation and permutation-based importance analysis reveals that certain features retain complementary discriminative power even when their standalone importance appears low. These findings highlight the importance of principled feature design and provide insight into feature vulnerability and resilience, offering guidance towards relatively noise-resilient and interpretable feature selection for next-generation radar classification systems.