# Semi-Supervised GNSS Scintillations Detection Based on DeepInfomax
###### Abstract  
This work focuses on a machine learning based detection of ionospheric scintillation events affecting Global Navigation Satellite System (GNSS) signals. We here extend the recent detection results based on Decision Trees, designing a semi-supervised detection system based on the DeepInfomax approach recently proposed. The paper shows that it is possible to achieve good classification accuracy while reducing the amount of time that human experts must spend manually labelling the datasets for the training of supervised algorithms. The proposed method is scalable and reduces the required percentage of annotated samples to achieve a given performance, making it a viable candidate for a realistic deployment of scintillation detection in software defined GNSS receivers. s 2020 10 381 2020 2019 2020  
Such approach was originally proposed in [8; 9], where the authors use an automatic scintillation detector based on a supervised machine learning technique called support vector machine (SVM), respectively for amplitude and phase scintillation. This approach provides good detection results but introduces a Fast Fourier Tranform (FFT) operation on the data over windows of 3 min, thus reducing the temporal resolution of the detection results. A second disadvantage is that the input data need to be pre-filtered at an elevation mask of 30\({}^{\circ}\) to reduce multipath false alarms, at the expenses of a potential waste of useful and important information. An updated version of the algorithm is presented in [10]. Other works propose using decision tree and random forest algorithms and low level signal observables, such as the in-phase and quadrature correlation outputs of the receiver tracking loop [11; 12]. This approach is able to reduce the false alarms rate due to the ambiguity between scintillation and multipath typical of the classical approaches based on the analysis of amplitude scintillation index and provide an early run-time alert. Another advantage is that, by exploiting the high rate correlator outputs, a finer time resolution in detection is obtained [13; 14]. A more generic survey of data mining techniques is given in [15]. However, this method does not only rely on GNSS observations, but also includes external data, such as sensors and online forecast services. The biggest problem with machine learning based classification is that datasets must be labeled manually by experts, wasting precious resources in terms of time, and thus preventing large scale scalability of the method [8; 11]. The techniques proposed in the literature and described above partially solve the problem as they are based on fully-supervised learning: a significant portion of input data must be manually labeled to assist the learning process of the algorithm. We here propose to switch to a paradigm centered around semi-supervised learning, in which in contrast to fully supervised learning the label information is not required for all the data, and some entries can be thus classified with an \"_unknown_\" label. In recent years, deep learning based solutions [16] for classification tasks have seen tremendous improvements in terms of performance. Neural networks in particular, however, have been rarely used as detection algorithms, one exception being [17]. Nevertheless, they have been successfully deployed for complex modeling of electron content in the ionosphere [18; 19]. Usually, deep learning based semi-supervised classifiers (see, for example, [20]), reduce the dimensionality of the unlabeled data \(X\) using unsupervised learning, then build a classifier using only the compressed labeled datapoints \(F\) for the datapoints where the corresponding label \(Y\) is available, as depicted in the scheme of Figure 1.  
In this paper, we make use of the DeepInfomax [21] approach in which a variational representation of mutual information [22] is used to build an unsupervised, information theoretic, feature extractor.