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 superior performance of the proposed solutions is primarily attributable to the enhanced network architecture they are based on, which facilitates more efficient learning, which consequently leads to more accurate predictions, when compared to Long-Short Term Memory (LSTM) approach. In this way, the proposed methods allow navigation in GNSS-denied scenarios without relying on expensive hardware (e.g., LiDARs or cameras).