Seasonal ionospheric scintillation analysis during increasing solar activity at mid-latitude

Ahmed, Wasiu Akande; Wu, Falin; Agbaje, Ganiyu Ishola; Ednofri, Ednofri; Marlia, Dessi; Zhao, Yan

doi:10.1117/12.2278224

Cited by 4 publications

(4 citation statements)

References 5 publications

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“…Figure 6 where accuracy represents the number of correct classifications made by the model as a proportion of all data, precision represents the accuracy of the model in judging potential events as occurring blinks, and recall refers to the percentage of correctly predicted classifications labelled 1 as a proportion of the true categories. Precision and recall are sometimes contradictory, and they are considered together using the F-score [35], which is Precision + Recall (12) where accuracy represents the number of correct classifications made by the model as a proportion of all data, precision represents the accuracy of the model in judging poten-tial events as occurring blinks, and recall refers to the percentage of correctly predicted classifications labelled 1 as a proportion of the true categories. Precision and recall are sometimes contradictory, and they are considered together using the F-score [35], which is taken as a weighted average of them, with a higher F-score indicating a more effective classification model.…”

Section: Evaluation Criteriamentioning

confidence: 99%

“…Data indicate that satellite communication near the polar and equator regions can be unreliable, with receivers often unable to capture the transmitted signal, resulting in interrupted navigation [11]. Additionally, there is a seasonal trend in the variation in ionospheric scintillation [12] throughout the year, with a higher frequency of occurrences during the spring and autumn equinoxes. The scintillation is particularly intense during the spring equinox.…”

Section: Introductionmentioning

confidence: 99%

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Automatic GNSS Ionospheric Scintillation Detection with Radio Occultation Data Using Machine Learning Algorithm

Ji,

Jin,

Zhen

et al. 2023

Applied Sciences

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Ionospheric scintillation often occurs in the polar and equator regions, and it can affect the signals of the Global Navigation Satellite System (GNSS). Therefore, the ionospheric scintillation detection applied to the polar and equator regions is of vital importance for improving the performance of satellite navigation. GNSS radio occultation is a remote sensing technique that primarily utilizes GNSS signals to study the Earth’s atmosphere, but its measurement results are susceptible to the effects of ionospheric scintillation. In this study, we propose an ionospheric scintillation detection algorithm based on the Sparrow-Search-Algorithm-optimized Extreme Gradient Boosting model (SSA-XGBoost), which uses power spectral densities of the raw signal intensities from GNSS occultation data as input features to train the algorithm model. To assess the performance of the proposed algorithm, we compare it with other machine learning algorithms such as XGBoost and a Support Vector Machine (SVM) using historical ionospheric scintillation data. The results show that the SSA-XGBoost method performs much better compared to the SVM and XGBoost models, with an overall accuracy of 97.8% in classifying scintillation events and a miss detection rate of only 12.9% for scintillation events with an unbalanced GNSS RO dataset. This paper can provide valuable insights for designing more robust GNSS receivers.

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Section: Evaluation Criteriamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Automatic GNSS Ionospheric Scintillation Detection with Radio Occultation Data Using Machine Learning Algorithm

Ji,

Jin,

Zhen

et al. 2023

Applied Sciences

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“…Non-uniform electrical charge distribution in the ionosphere is responsible for satellite communication delay and many other forms of signal interference such as rapid fluctuations, reflection, and refraction. 19 The non-uniform of electron density in space is the reason why the space index varies from one point to the other. This degrades the satellite signal that transmits very sensitive data which embedded communication, positioning, and timing information.…”

Section: Satellite Signal Transmission Characteristics and Modulation...mentioning

confidence: 99%

Hybrid threshold combining and maximum ratio combining model for satellite communication over composite Rayleigh and Rician fading channel

Ahmed

Agbaje

et al. 2021

Satell Commun Network

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Communication through satellite is of great task and importance especially in the world of telecommunication infrastructure. It plays a leading role for future communication systems. However, the performance of the satellite communication system is dominated by the channel conditions overwhelmed by multipath fading and ionospheric scintillation effects. Maximum ratio combining (MRC) previously used to tackle this problem is characterized with hardware complexity resulted in long processing time, while threshold combining (TC) with simple hardware display poor performance. Thus, evaluation of hybrid TC-MRC, with closed-form expression over composite Rayleigh and Rician fading channel is necessary. In this paper, TC and MRC are combined to improve the signal quality and performance. The data are modulated using M-ary quadrature amplitude modulation and transmitted over the combined effects. The received signals at varying paths are scanned to select the best paths. A mathematical expression using the probability density function of composite Rayleigh and Rician fading channel for mean integrated square error (MISE) is derived. The model is efficiently simulated and the performance is estimated. The results show that the hybrid TC-MRC model achieves lower MISE and processing time compared to threshold combiner and even maximum ratio combiner, and thereby enhance the performance of the satellite communication system.

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“…Ionospheric scintillation is a known predominant propagation impairment at L-band frequencies [2]. The existence of irregularities in the ionosphere distorts radio signals [3] as they pass through the ionosphere from a satellite in space, thereby leading to phase mixing, which creates refraction, reflection, and scattering [4]. It is important to note that the small-and large-scale ionospheric irregularities cause scintillations in the trans-ionospheric communication and navigation signals [5], depending on the time of the day [6], season, space weather activities, and geographical locations [7].…”

Section: Introductionmentioning

confidence: 99%

Mitigation of Ionospheric Scintillation Effects on GNSS Signals with VMD-MFDFA

Ahmed

Marlia

et al. 2019

Remote Sensing

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Severe scintillations degrade the satellite signal intensity below the fade margin of satellite receivers thereby resulting in failure of communication, positioning, and navigational services. The performance of satellite receivers is obviously restricted by ionospheric scintillation effects, which may lead to signal degradation primarily due to the refraction, reflection, and scattering of radio signals. Thus, there is a need to develop an ionospheric scintillation detection and mitigation technique for robust satellite signal receivers. Hence, variational mode decomposition (VMD) is proposed. VMD addresses the problem of ionospheric scintillation effects on global navigation satellite system (GNSS) signals by extracting the noise from the radio signals in combination with multifractal detrended fluctuation analysis (MFDFA). MFDFA helps as a criterion designed to detect and distinguish the intrinsic mode functions (IMFs) into noisy (scintillated) and noise-free (non-scintillated) IMF signal components using the MFDFA threshold. The results of the proposed method are promising, reliable, and have the potential to mitigate ionospheric scintillation effects on both the synthetic (simulated) and real GNSS data obtained from Manado station (latitude 1.34° S and longitude 124.82° E), Indonesia. From the results, the effectiveness of VMD-MFDFA over complementary ensemble empirical mode decomposition with MFDFA (CEEMD-MFDFA) is an indication of better performance.

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Seasonal ionospheric scintillation analysis during increasing solar activity at mid-latitude

Cited by 4 publications

References 5 publications

Automatic GNSS Ionospheric Scintillation Detection with Radio Occultation Data Using Machine Learning Algorithm

Automatic GNSS Ionospheric Scintillation Detection with Radio Occultation Data Using Machine Learning Algorithm

Hybrid threshold combining and maximum ratio combining model for satellite communication over composite Rayleigh and Rician fading channel

Mitigation of Ionospheric Scintillation Effects on GNSS Signals with VMD-MFDFA

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