# Situational Awareness: Mapping Interference Sources in Real-Time Using a Smartphone App
## 7 Conclusions and Expected Developments  
In this paper, a new portable and easily deployable real-time RFI detector named NGeneApp has been presented. Particular emphasis has been dedicated to the development work, i.e., to the porting of a software receiver to an Android-based smartphone. The RFI detection functionality has been implemented by means of a combination of pre- and post-correlation techniques, properly calibrated with lab instruments. Furthermore, its effectiveness in catching RF disturbances in real-time and real-life environment has been demonstrated with a live test campaign. In this regard, three main usage examples have been presented and in all of the considered situations, NGeneApp was shown to be able to detect interferences successfully. For instance, narrow band disturbing signals and unexpected spikes, likely unintentional, have been noticed walking in an urban scenario. Using NGeneApp for dynamic tests, the source of an interfering signal coming from space has been quickly identified in a non-operational GPS satellite. Finally, NGeneApp has been employed to quickly recognize potential sources of interference in complex integration systems.  
According to the achieved results, NGeneApp shows to be a simple and portable tool to check the presence of interference in the environment in real-time. It has been developed with the potential of being an _in-field sensor_ in a de-centralized, unstructured, interference monitoring network. In this network, several sensors spread across wide areas should monitor local GNSS interference, then transmit to a remote server their measurements collected whenever an interference even is detected. Following a crowdsourcing philosophy, a short-delay post-processing implemented at the server side on the data received from sensors would allow drawing a near-real-time map of the interference over a certain area, in order to create conditions of situational awareness. Longer time observations would allow inferring about interference persistence or periodicity and source localization. On the sensor side, the software approach easily enables the potential of enhancing the sensitivity and accuracy of the detection module, for example implementing other detection metrics or refining the detection rules.  
The current version of NGeneApp is able to handle up to maximum 12 channels, which satisfies the interference monitoring requirements. The possibility of using other wider-bandwidth front-ends is currently under investigation, in order to improve the capability of classification and identification of the interference sources. The development of the backend server with monitoring capabilities is another future investigation direction.  
H.L.N. is the principal developer of the software application. [PERSON] is the software defined radio expert who guided the porting of the receiver code. [PERSON] supervised the implementation and testing of the interference detection algorithms. [PERSON] provided comments and suggestions for the presentation and exploitation of the work done.  
This work has been partly supported by the Italian Ministry of Foreign Affairs and International Cooperation (MAECI) in the framework of the project GILD (Progetto di Grande Rilevanza-Protocolo Esecutivo di Cooperazione Scientifica e Tecnologica bilaterale Italia-Vietnam 2017-2019, NDT.38.ITA/18).  
The authors declare no conflict of interest.