STLC-Net: A Short-Window Temporal Latent-Consistency Network for ADS-B Trajectory Anomaly Detection

Automatic Dependent Surveillance–Broadcast (ADS-B) is a key source of air traffic surveillance data, but its open broadcast mechanism and possible trajectory-data inconsistencies may reduce surveillance reliability. This study proposes a Short-window Temporal Latent-Consistency Network (STLC-Net) for window-level anomaly detection in real-world ADS-B surveillance data. Raw ADS-B messages are transformed into physically interpretable kinematic features and segmented into short sliding windows to capture local motion inconsistencies. STLC-Net combines LSTM-based temporal encoding with an encoder–decoder–encoder latent-consistency mechanism, and fuses reconstruction and latent-consistency discrepancies to produce anomaly scores. Experimental results show that STLC-Net achieves a PR-AUC of 0.952540, precision of 1.0, recall of 0.882682, and F1-score of 0.937685 under highly imbalanced conditions. Ablation studies further confirm the effectiveness of short-window representation, temporal modeling, and latent-consistency learning. These results indicate that STLC-Net provides an effective and physically interpretable framework for detecting rare kinematic anomalies in ADS-B trajectory surveillance data.