Effect of Radio Frequency Interference (RFI) on the Precision of GPS Relative Positioning

Idris, Ahmad Norhisyam; Sathyamoorthy, Dinesh; Suldi, Azman Mohd; Hamid, Juazer Rizal Abdul

doi:10.1088/1755-1315/18/1/012035

Cited by 5 publications

(3 citation statements)

References 1 publication

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“…A study of the performance of GPS receivers has been made comprehensively in [4]. However, since a GPS receiver can be affected by EMI [5], an AEV may take a wrong direction if the supporting GPS fails to provide correct data. The sources of electromagnetic field are not only on-board electrical or electronic devices but also transmitter stations in the environment as it is indicated in [6].…”

Section: Introductionmentioning

confidence: 99%

An Autoregressive Model of Electromagnetic Disturbances in An Autonomous Electric Vehicle’s Route

Trihatmo,

Hendrantoro,

Septiawan

et al. 2024

JAREE

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Electromagnetic Interference (EMI) can cause a malfunction of on-board electronic circuits in an autonomous electric vehicle and supporting electronic devices located in the environment of autonomous electric vehicles as well. In order to navigate an autonomous electric vehicle safely, it is important to have electromagnetic field characteristic in the environment. Since the information of electromagnetic field characteristic is hard to find, it needs to be modeled. This paper presents a model of electromagnetic field characteristic that is generated by using autoregression in order to estimate potential EMI. The EMI estimation is based on electromagnetic characteristic in an environment. Unlike other applications that use time history of data to build a model, we present a spatial electromagnetic field strength data in a previous route to estimate the future data in a new route. To obtain historical data for auto-regression process, we measured electric field strengths along a circular route in a campus near Jakarta. This surrounding environment represents a typical area of suburbs. The input variables for auto-regression process are the first 27 correlated data of 155 measured data. The result shows that the use of 13 predictor coefficient produces a variance of prediction error near to zero, with an improvement from maximum prediction error of 15.1257 to prediction error of 0.1862.

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Section: Introductionmentioning

confidence: 99%

An Autoregressive Model of Electromagnetic Disturbances in An Autonomous Electric Vehicle’s Route

Trihatmo,

Hendrantoro,

Septiawan

et al. 2024

JAREE

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“…Typically, the raw C/N0 values with the internal interferences range from 40 to 50 decibel-Hertz (dB-Hz) depending on the thermal noises of the antenna and receiver hardware, and they can be reduced by the external RFI and jamming (Borio D et al 2016;Mackey S et al 2017;Paziewski J et al 2019). Exactly, the affected GNSS receivers below 40 dB-Hz can degrade the user positioning accuracy, continuity, and integrity (Idris A N et al 2013(Idris A N et al , 2014.…”

Section: Introductionmentioning

confidence: 99%

Statistical Analysis and Effects of Radio Frequency Interference in GPS Signal Quality in Thailand

Sophan,

Supnithi,

Myint

et al. 2024

Preprint

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The radio frequency interference (RFI) in global navigation satellite system (GNSS) signals has recently received much attention in the GNSS community because of frequent jamming issues. The carrier-to-noise density ratio (C/N0) is one of the common parameters to indicate the signal quality. In this work, we aim to analyze the occurrences of local RFI effects in areas surrounding the Suvarnabhumi International Airport as well as remote areas. Specifically, the percentage of RFI levels and raw C/N0 values are analyzed based on the modified RFI detection. The estimated RFI values are created from the raw C/N0 data, followed by the RFI levels generated by the empirical data. We validate the modified RFI detection by using the GNSS reference stations at the urban, suburban, and outside the capital city in Thailand. The user positioning errors with the high (severe) RFI levels are investigated based on the single point positioning (SPP) and real-time kinematics (RTK). From the experimental simulations, the high RFI levels at the urban are higher than those at the suburban. As expected, the statistical analysis covering COVID-19 (2019 to 2023) shows that the high RFI levels in June 2023 (post COVID-19) are more than those in June 2020 and 2021 (lockdown COVID-19) about twofold. Additionally, the SPP and RTK errors with the medium/high RFI levels have the error spikes in the centimeter to meter levels. The RTK performance with the fixed status can be affected and then returned to the float status as well.

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“…The low received power of GPS signals makes it highly vulnerable to threats such as unintentional and intentional interferences. The sources of unintentional interference may be in the form of ionospheric effects, signal blockage, and multipath [4]. On the other hand, the intentional interference can be erupted in the form of jamming and spoofing that makes misleading information about the user position.…”

Section: Introductionmentioning

confidence: 99%

Disruptive GNSS Signal detection and classification at different Power levels Using Advanced Deep-Learning Approach

Elango

Ujan

Ruotsalainen

2022

2022 International Conference on Localization and GNSS (ICL-GNSS)

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Although the Global Navigation Satellite System (GNSS) technology provides an excellent benefit in different critical areas such as civilian, aviation, military, and commercial applications, it is highly vulnerable to various signal disruptions causing significant positioning errors. One of the major threats to a GNSS receiver is the intentional interference known as jamming. A Jammer significantly disrupts the normal functioning of a GNSS receiver, at the acquisition, tracking, and positioning stages. The foremost important step to combat against jamming of GNSS signals is the early detection and characterization of the interfering signals to guarantee the Quality of Service (QoS). This paper presents a robust Deep-Learning (DL) based technique using transfer learning to characterize the type of disruption in GNSS signal based on time-frequency analysis. To this end, a pretrained Convolutional Neural Network (CNN) is used to extract the informative features from the scalogram of the received signals. Further, a fully connected layer followed by a Soft-Max activation function is deployed to classify the signals. In this work, the Signal of Interest (SoI) is a synthetic GPS signal generated by a GNSS simulator. In our experiment, the GPS signal is combined with different kinds of jamming, spoofing, and multipath signals. Moreover, the proposed classification approach can recognize not only the various kinds of jammers such as ones producing Continuous Wave Interference (CWI), Multi-CWI (MCWI), Chirp Interference (CI), and Pulse interference (PI) but also the inclusion of Additive White Gaussian Noise (AWGN). Besides that, the effect of five pre-trained CNNs, namely, AlexNet, GoogleNet, ResNet-18, VGG-16, and MobileNet-V2, is evaluated on classification accuracy. The GNSS signal and its seven disruptive variants are recorded at three different power levels such as low, medium, and high. The medium power level signal is used for training and the testing has been carried out for unseen data set of low, high, and mixed power level. From the simulation results, it has been observed that MobileNet-V2 has performed better than other techniques with an accuracy of 99.8%. Finally, the trained MobileNet-V2 is used to predict the unseen data type generated at different Jamming to signal Ratios (JSRs).

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Effect of Radio Frequency Interference (RFI) on the Precision of GPS Relative Positioning

Cited by 5 publications

References 1 publication

An Autoregressive Model of Electromagnetic Disturbances in An Autonomous Electric Vehicle’s Route

An Autoregressive Model of Electromagnetic Disturbances in An Autonomous Electric Vehicle’s Route

Statistical Analysis and Effects of Radio Frequency Interference in GPS Signal Quality in Thailand

Disruptive GNSS Signal detection and classification at different Power levels Using Advanced Deep-Learning Approach

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