## 6 Conclusions and Future Developments  
This paper describes an approach to detect and classify different types of interferences on GNSS signals based on the application of ML/DL algorithms, where detection means the capability to reveal the presence of an interference condition in the processed GNSS signals, while classification refers to the capability to distinguish among different types of interference (i.e., wideband, Gaussian, and narrowband in this study). In particular, the impact of three main parameters was considered in the study: (1) the bit-depth at which the GNSS signal was played back before it was processed by the GNSS receiver, (2) the power level of the interference signal, and (3) the number of different GNSS satellites used to detect and classify the interference. This study used ML algorithms and neural networks/Deep Learning (DL) algorithms. The ML algorithms were a Decision Tree (DT), Random Forest (RaF), and Extremely Randomized Trees (ERT). The DL was a Convolutional Neural Network (CNN). The results show that the CNN is able to obtain almost perfect detection accuracy with high and medium levels of interference power and bit-depth equal or superior to 4 bits with the wideband and Gaussian interference and eight GNSS satellites. However, the accuracy drops with a bit-depth equal to 1 or 2, low interference level, and narrowband interference because the impact of the interference is less distinguishable in the GNSS receiver and because the bit-depth reduction removes discriminating information for the CNN classifier. Similar results are obtained for the interference classification task where perfect (100%) or almost perfect (greater than 99%) accuracy is generally obtained for bit-depth greater and including 4 bits and interference levels are medium or high, but it drops significantly for a low interference level and bit-depth of 1 or 2. The DT and RaF generally perform worst than the CNN. It is noted that the ERT algorithm often achieves a similar level of accuracy to the CNN with less computing resources, which could support its use in operational scenarios, which requires computational efficiency. The use of the number of satellites also has a significant impact. The classification performance degrades significantly when only the information from one or two satellites is used. The results show that the use of six satellites or eight satellites is quite similar, supporting the concept that with this specific data set, six satellites would be enough to distinguish with high accuracy the different types of interference. Then, there is a trade-off between the need to process more information from a larger number of satellites with the need for a high classification accuracy. This paper has also discussed the limitations of the proposed approach. Even if real-data from GNSS constellations was used in the study in a significant amount of time, a dynamic environment where the signal quality from the different satellites changes frequently (e.g., a GNSS receiver mounted on a vehicle driving in an urban environment) may be challenging for the proposed approach where a fixed number of satellites is used. Then, an adaptive algorithm based on the quality of the received signal could be used to determine in a dynamic way the optimal number of satellites and address the trade-off mentioned above. The evaluation of the approach proposed in this paper to a vehicular dynamic context will be the objective of future extensions of this study. Future developments could go in different directions. One direction would be to investigate the proposed approach on a dynamic environment (e.g., vehicle moving in an urban environment) where the propagation conditions change frequently. Then, new filtering steps should be investigated to determine the optimal thresholds in the data pre-processing phase. As suggested in the main body of this paper, a hybrid approach of shallow and deep ML algorithms could address the need for timely results and computing efficiency.