Identifying GNSS Signals Based on Their Radio Frequency (RF) Features—A Dataset with GNSS Raw Signals Based on Roof Antennas and Spectracom Generator

Ferre, Ruben Morales; Wang, Wenbo; Sanz-Abia, Alejandro; Lohan, Elena Simona

doi:10.3390/data5010018

Cited by 13 publications

(6 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…where r(n) is the complex sampled signal and r * (n) is the conjugate of r(n). TKEO has been previously used in the context of RFF in GNSS in [3] with promising results. It is also one of the feature extractors selected in our study.…”

Section: Teager-kaiser Energy Operator (Tkeo)mentioning

confidence: 99%

“…The radio frequency fingerprinting (RFF) concept refers to the process of identifying the hardware (HW) characteristic and HW-specific features or signatures embedded in the radio frequency (RF) waves transmitted over a wireless channel [ 1 , 2 , 3 , 4 ]. In a strict sense, RFF refers only to the transmitter-specific HW features.…”

Section: State-of-the-art-review and Paper Contributionsmentioning

confidence: 99%

“…Furthermore, the receiver from Figure 8 also has its own HW elements such as front-end filtering, analog-to-digital (ADC) conversion, local oscillators, and power amplification, and each of these elements will act as additional distortions to the individual transmitter features, as they will be common to all signals

found in the received signal

(see Equation ( 16 )). As shown in [ 3 ], the same GNSS data from GNSS satellites collected with two different antennas give different fingerprints. This means that, in order to be able to fully identify a GNSS transmitter, one should be able to remove the receiver front-end features from the analysis.…”

Section: Equivalent Block Diagrams For Gnss and Spoofing Signalsmentioning

confidence: 99%

See 2 more Smart Citations

A Survey of Spoofer Detection Techniques via Radio Frequency Fingerprinting with Focus on the GNSS Pre-Correlation Sampled Data

Wang

Sánchez

Caparra

et al. 2021

Sensors

Self Cite

View full text Add to dashboard Cite

Radio frequency fingerprinting (RFF) methods are becoming more and more popular in the context of identifying genuine transmitters and distinguishing them from malicious or non-authorized transmitters, such as spoofers and jammers. RFF approaches have been studied to a moderate-to-great extent in the context of non-GNSS transmitters, such as WiFi, IoT, or cellular transmitters, but they have not yet been addressed much in the context of GNSS transmitters. In addition, the few RFF-related works in GNSS context are based on post-correlation or navigation data and no author has yet addressed the RFF problem in GNSS with pre-correlation data. Moreover, RFF methods in any of the three domains (pre-correlation, post-correlation, or navigation) are still hard to be found in the context of GNSS. The goal of this paper was two-fold: first, to provide a comprehensive survey of the RFF methods applicable in the GNSS context; and secondly, to propose a novel RFF methodology for spoofing detection, with a focus on GNSS pre-correlation data, but also applicable in a wider context. In order to support our proposed methodology, we qualitatively investigated the capability of different methods to be used in the context of pre-correlation sampled GNSS data, and we present a simulation-based example, under ideal noise conditions, of how the feature down selection can be done. We are also pointing out which of the transmitter features are likely to play the biggest roles in the RFF in GNSS, and which features are likely to fail in helping RFF-based spoofing detection.

show abstract

Section: Teager-kaiser Energy Operator (Tkeo)mentioning

confidence: 99%

Section: State-of-the-art-review and Paper Contributionsmentioning

confidence: 99%

found in the received signal

Section: Equivalent Block Diagrams For Gnss and Spoofing Signalsmentioning

confidence: 99%

See 1 more Smart Citation

A Survey of Spoofer Detection Techniques via Radio Frequency Fingerprinting with Focus on the GNSS Pre-Correlation Sampled Data

Wang

Sánchez

Caparra

et al. 2021

Sensors

Self Cite

View full text Add to dashboard Cite

show abstract

“…For WII, frequency domain representations have performed better since the frequency components of a signal remain more distinctive even under lower SNR. WII can appear as different use cases, such as classifying different UAVs [4], [9], [11], [20], detecting/classifying different Radar Waveforms [2], [21], identifying specific technologies in the ISM band [8], securing Global Navigation Satellite Systems (GNSS) signals [22], and more. Like time-domain representations, frequencydomain representations can also be kept as a vector of values (such as DFT coefficients) used in [4] or further transforms can be applied to make a PSD image, or STFT image (also known as a spectrogram), used in [9], [11] and others.…”

Section: Literature Reviewmentioning

confidence: 99%

Deep Learning Radio Frequency Signal Classification with Hybrid Images

Elyousseph¹,

Altamimi²

2021

Preprint

View full text Add to dashboard Cite

In recent years, Deep Learning (DL) has been successfully applied to detect and classify Radio Frequency (RF) Signals. A DL approach is especially useful since it identifies the presence of a signal without needing full protocol information, and can also detect and/or classify non-communication waveforms, such as radar signals. In this work, we focus on the different pre-processing steps that can be used on the input training data, and test the results on a fixed DL architecture. While previous works have mostly focused exclusively on either time-domain or frequency domain approaches, we propose a hybrid image that takes advantage of both time and frequency domain information, and tackles the classification as a Computer Vision problem. Our initial results point out limitations to classical pre-processing approaches while also showing that it's possible to build a classifier that can leverage the strengths of multiple signal representations.

show abstract

“…A GNSS-signals database is described in [2], where raw signals were collected via roof antennas and a Spectracom simulator for general-purpose uses, including analysis for fingerprint-based applications. Radio-frequency fingerprinting has been shown to be promising for identifying transmitters and receivers, which can be used in anti-spoofing and anti-jamming solutions.…”

mentioning

confidence: 99%