Estimation of Scintillation Indices: A Novel Approach Based on Local Kernel Regression Methods

Ouassou, Mohammed; Kristiansen, Oddgeir; Gjevestad, Jon Glenn Omholt; Jacobsen, Knut Stanley; Andalsvik, Yngvild Linnea

doi:10.1155/2016/3582176

Cited by 7 publications

(6 citation statements)

References 32 publications

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“…3(b) and Fig. 4 [7,24]. The local average of Vi was obtained by passing Vi through a 3 rd order Butterworth low-pass filter with a very low cut-off frequency of 0.04 Hz.…”

Section: Resultsmentioning

confidence: 99%

Experimental study of the turbulence effect on underwater optical wireless communications

et al. 2018

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Underwater optical wireless communications (UOWC) performance is affected by turbulence. However, not much research has been carried out to estimate the probability density function (PDF) of the received optical power. In this paper, we investigate the turbulence effect on UOWC using a new experimental setup with a variable link span in a water pool. Different turbulence levels are created by changing the temperature and the rate of an injected water flow in the pool in order to obtain the PDF. Results show that lognormal distribution fits well with the measured PDF up to the scintillation index value of 0.07. In UOWC systems the link span is one of the main factor influencing fluctuations of the received optical power, which has not been investigated. In this work, we obtain the scintillation index and turbulence induced power loss for a range of turbulence strengths and for a transmission link span of up to 12 m. Finally, we show that there is a good agreement between the experimental and simulated results.

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“…3(b) and Fig. 4 [7,24]. The local average of Vi was obtained by passing Vi through a 3 rd order Butterworth low-pass filter with a very low cut-off frequency of 0.04 Hz.…”

Section: Resultsmentioning

confidence: 99%

Experimental study of the turbulence effect on underwater optical wireless communications

et al. 2018

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“…Their computation requires averaging and detrending operations and, in general, algorithms with complex tuning, which are time consuming, computationally expensive, and potentially introduce heavy artifacts, thus altering the scintillation detection process (Mushini et al., 2012). In particular, although detrending the phase by means of a Butterworth filter is the de facto standard (Van Dierendonck, Klobuchar, & Hua, 1993), many authors have shown limitations when dealing with polar scintillations, related to the choice of the filter cutoff frequency, which should be related to the local features of the ionosphere (Forte, 2005; Mushini et al., 2012; Ouassou et al., 2016). A wrong and non‐adaptive cutoff frequency ultimately alters the value of the traditional indices up to the point in which the scintillation detection is completely mistaken.…”

Section: Scintillation Detectionmentioning

confidence: 99%

“…(2012). Alternative scintillation indices, based on non‐parametric local regression with bias Corrected Akaike Information Criteria (AICC) have been proposed by Ouasson et al ., reducing the computational load of wavelet analysis and superior handling of discontinuities (Ouassou et al., 2016). Recently, the Adaptive Local Iterative Filtering (ALIF) method has been proposed to analyse phase time series, improving scale resolution with respect to wavelet transform (Piersanti et al., 2018).…”

Section: Scintillation Detectionmentioning

confidence: 99%

Survey on signal processing for GNSS under ionospheric scintillation: Detection, monitoring, and mitigation

et al. 2020

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Ionospheric scintillation is the physical phenomena affecting radio waves coming from the space through the ionosphere. Such disturbance is caused by ionospheric electron‐density irregularities and is a major threat in Global Navigation Satellite Systems (GNSS). From a signal‐processing perspective, scintillation is one of the most challenging propagation scenarios, particularly affecting high‐precision GNSS receivers and safety critical applications where accuracy, availability, continuity, and integrity are mandatory. Under scintillation, GNSS signals are affected by amplitude and phase variations, which mainly compromise the synchronization stage of the receiver. To counteract these effects, one must resort to advanced signal‐processing techniques such as adaptive/robust methods, machine learning, or parameter estimation. This contribution reviews the signal‐processing landscape in GNSS receivers, with emphasis on different detection, monitoring, and mitigation problems. New results using real data are provided to support the discussion. To conclude, future perspectives of interest to the GNSS community are discussed.

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“…This algorithm is different from the standard routines to compute S4, where signal intensity I is used in Equation (1) instead of C/N 0 [21]. However, a benchmarking indicated the effective S4 values computed by Equation (1) are highly correlated with the S4 generated from the NovAtel GPStation-6 receiver, a commercial off-the-shell (COTS) ionospheric TEC and scintillation monitor.…”

Section: Methods and Infrastructurementioning

confidence: 99%

Satellite Formation Flight Simulation Using Multi-Constellation GNSS and Applications to Ionospheric Remote Sensing

Peng

Scales

2019

Remote Sensing

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The Virginia Tech Formation Flying Testbed (VTFFTB) is a global navigation satellite system (GNSS)-based hardware-in-the-loop (HIL) simulation testbed for spacecraft formation flying with ionospheric remote sensing applications. Past applications considered only the Global Positioning System (GPS) constellation. The rapid GNSS modernization offers more signals from other constellations, including the growing European system-Galileo. This study presents an upgrade of VTFFTB with the incorporation of Galileo and the associated enhanced capabilities. By simulating an ionospheric plasma bubble scenario with a pair of LEO satellites flying in formation, the GPS-based simulations are compared to multi-constellation GNSS simulations including the Galileo constellation. A comparison between multi-constellation (GPS and Galileo) and single-constellation (GPS) shows the absolute mean and standard deviation of vertical electron density measurement errors for a specific Equatorial Spread F (ESF) scenario are decreased by 32.83% and 46.12% with the additional Galileo constellation using the 13 July 2018 almanac. Another comparison based on a simulation using the 8 March 2019 almanac shows the mean and standard deviation of vertical electron density measurement errors were decreased further to 43.34% and 49.92% by combining both GPS and Galileo data. A sensitivity study shows that the Galileo electron density measurements are correlated with the vertical separation of the formation configuration. Lower C/N 0 level increases the measurement errors and scattering level of vertical electron density retrieval. Relative state estimation errors are decreased, as well by utilizing GPS L1 plus Galileo E1 carrier phase instead of GPS L1 only. Overall, superior performance on both remote sensing and relative navigation applications is observed by adding Galileo to the VTFFTB.

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Estimation of Scintillation Indices: A Novel Approach Based on Local Kernel Regression Methods

Cited by 7 publications

References 32 publications

Experimental study of the turbulence effect on underwater optical wireless communications

Experimental study of the turbulence effect on underwater optical wireless communications

Survey on signal processing for GNSS under ionospheric scintillation: Detection, monitoring, and mitigation

Satellite Formation Flight Simulation Using Multi-Constellation GNSS and Applications to Ionospheric Remote Sensing

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