A Navigation Signals Monitoring, Analysis and Recording Tool: Application to Real-Time Interference Detection and Classification

Mehr, Iman Ebrahimi; Minetto, Alex; Dovis, Fabio

doi:10.33012/2023.19391

Cited by 1 publication

(1 citation statement)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Classification refers indeed to those algorithms that are able to discriminate the received interference according to pre-defined classes based on specific signal features. In this regard, researchers have recently applied artificial intelligence (AI)/machine learning (ML) algorithms to classify interference: we can find examples of this in [ 46 , 47 , 48 , 49 , 50 , 51 ]. Convolutional neural network (CNN) architecture, commonly used for image classification, can be applied to this problem.…”

Section: Theoretical Backgroundmentioning

confidence: 99%

GNSS Radio Frequency Interference Monitoring from LEO Satellites: An In-Laboratory Prototype

Troglia Gamba,

Polidori,

Minetto

et al. 2024

Sensors

Self Cite

View full text Add to dashboard Cite

The disruptive effect of radio frequency interference (RFI) on global navigation satellite system (GNSS) signals is well known, and in the last four decades, many have been investigated as countermeasures. Recently, low-Earth orbit (LEO) satellites have been looked at as a good opportunity for GNSS RFI monitoring, and the last five years have seen the proliferation of many commercial and academic initiatives. In this context, this paper proposes a new spaceborne system to detect, classify, and localize terrestrial GNSS RFI signals, particularly jamming and spoofing, for civil use. This paper presents the implementation of the RFI detection software module to be hosted on a nanosatellite. The whole development work is described, including the selection of both the target platform and the algorithms, the implementation, the detection performance evaluation, and the computational load analysis. Two are the implemented RFI detectors: the chi-square goodness-of-fit (GoF) algorithm for non-GNSS-like interference, e.g., chirp jamming, and the snapshot acquisition for GNSS-like interference, e.g., spoofing. Preliminary testing results in the presence of jamming and spoofing signals reveal promising detection capability in terms of sensitivity and highlight room to optimize the computational load, particularly for the snapshot-acquisition-based RFI detector.

show abstract