GNSS-Denied UAV Terrain Matching Navigation Based on the Autoencoder Network with Contrastive Learning

Reliable navigation is critical for UAVs operating in GNSS-denied environments, where conventional Inertial Navigation System/Global Navigation Satellite System (INS/GNSS) integrated navigation struggles to meet the requirements of high-reliability and long-endurance missions. As a passive and autonomous approach, terrain-aided navigation (TAN) offers strong concealment and a high degree of autonomy. However, most existing TAN methods rely on handcrafted features, which limit their ability to fully exploit multi-level terrain information, while sensitivity to elevation noise and attitude variations further degrades matching accuracy and robustness. To address these issues, this paper proposes a GNSS-denied UAV terrain matching navigation method based on an autoencoder network with contrastive learning. A Global–Local Dual-branch Feature Extraction Network (GL-DualNet) is designed to combine the local detail extraction capability of CNNs with the global dependency modeling ability of the Swin Transformer, enabling effective multi-scale terrain representation. In addition, an Autoencoder Contrastive Learning Model (ACLM) is developed to jointly optimize reconstruction and contrastive objectives, enabling unsupervised learning of terrain features with improved discriminability and robustness against noise and rotational disturbances. Experiments on a public terrain dataset show that the proposed method outperforms conventional terrain matching approaches under different noise levels, rotational disturbances, and search ranges, demonstrating its effectiveness and robustness for UAV navigation in complex GNSS-denied environments.