Lightweight multimodal fusion for UAV classification with explicit missing-modality modeling

Fine-grained unmanned aerial vehicle (UAV) type classification benefits from complementary sensors, yet real deployments face incomplete observations and heterogeneous stream reliability. We present a missing-aware fusion framework for RGB detections (YOLO11-S), Livox Avia LiDAR, 360° LiDAR, and radar-enhanced point clouds on MMAUD. Confidence–density scores are masked and renormalized so fusion weight is assigned only to observed streams (Eq. (2)); each absent stream still occupies a classifier slot via a learnable missing embedding that is not multiplied by the zero weight (Eq. (1)), keeping absence as a trainable signal rather than a numerical zero. On a time-stratified split, Tiny MLP (≈ 0.1 M fusion parameters) reaches 96.12% test accuracy in a representative seed-42 run. Over seeds 42–46, final test accuracy is 95.29 ± 0.60% without training-time modality dropout and 95.77 ± 0.90% with 30% training ModDrop (mean ± sample s.d.). Under matched 30% test missingness, accuracy remains 83.68% (seed 42). Protocol-aligned baselines and orthogonal ablations support the slot rule. Fusion-stage inference is about 2.1 ms per sample with pre-extracted features on an RTX 4090. Under our fixed split and frozen features—not as a challenge-leaderboard claim—compact missing-aware fusion is practical for heterogeneous UAV sensing under partial sensor availability.