The paper “Deep Learning Models for GNSS-denied Target Navigation”, co-authored by João Pedro Carvalho, LASIGE integrated, member among others, received the Best Paper Award at the 16th Doctoral Conference on Computing, Electrical and Industrial Systems (DoCEIS 2025), held in Portugal from 2 to 4 July 2025.

This work tackles the problem of target navigation in Global Navigation Satellite Systems (GNSS)-denied scenarios by adapting two Deep Learning (DL)-based approaches: the Temporal Fusion Transformer (TFT) and the Neural Hierarchical Interpolation for Time Series (NHITS). These methods are trained on custom created datasets from which the methods learn, after which they are able to make their own predictions. Obtained numerical results via simulations and real testbed reveal that, on the one hand, the proposed methods improve navigation accuracy and are less vulnerable to noise when compared to existing Machine Learning (ML) approaches. On the other hand, the results also exhibit a reduction in training time.

The paper is available here.