Wavelet and power spectrum analysis of global navigation satellite system multipath error modeling and mitigation

Ansari, Kutubuddin; Bae, Tae-Suk

doi:10.1002/sat.1405

Cited by 9 publications

(3 citation statements)

References 80 publications

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“…4 that the SNR observations are oscillating with the increase of elevation angle due to the multipath errors. If there are no multipath errors, the SNR measurements will smoothly rise with the increase of elevation angles 42 , 43 . The variation of the SNR plot shows that the strength of multipath signals varies between − 20 to 20 V/volts for L1 and L2 QZSS signals, while they are very low between − 5 to 5 V/volts for the L5 QZSS signal.…”

Section: Resultsmentioning

confidence: 99%

Quasi zenith satellite system-reflectometry for sea-level measurement and implication of machine learning methodology

Ansari¹,

Seok

Jamjareegulgarn

2022

Sci Rep

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The tide gauge measurements from global navigation satellite system reflectometry (GNSS-R) observables are considered to be a promising alternative to the traditional tide gauges in the present days. In the present paper, we deliver a comparative analysis of tide-gauge (TG) measurements retrieved by quasi-zenith satellite system-reflectometry (QZSS-R) and the legacy TG recordings with additional observables from other constellations viz. GPS-R and GLONASS-R. The signal-to-noise ratio data of QZSS (L1, L2, and L5 signals) retrieved at the P109 site of GNSS Earth Observation Network in Japan (37.815° N; 138.281° E; 44.70 m elevation in ellipsoidal height) during 01 October 2019 to 31 December 2019. The results from QZSS observations at L1, L2, and L5 signals show respective correlation coefficients of 0.8712, 0.6998, and 0.8763 with observed TG measurements whereas the corresponding root means square errors were 4.84 cm, 4.26 cm, and 4.24 cm. The QZSS-R signals revealed almost equivalent precise results to that of GPS-R (L1, L2, and L5 signals) and GLONASS-R (L1 and L2 signals). To reconstruct the tidal variability for QZSS-R measurements, a machine learning technique, i.e., kernel extreme learning machine (KELM) is implemented that is based on variational mode decomposition of the parameters. These KELM reconstructed outcomes from QZSS-R L1, L2, and L5 observables provide the respective correlation coefficients of 0.9252, 0.7895, and 0.9146 with TG measurements. The mean errors between the KELM reconstructed outcomes and observed TG measurements for QZSS-R, GPS-R, and GLONASS-R very often lies close to the zero line, confirming that the KELM-based estimates from GNSS-R observations can provide alternative unbiased estimations to the traditional TG measurement. The proposed method seems to be effective, foreseeing a dense tide gauge estimations with the available QZSS-R along with other GNSS-R observables.

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Section: Resultsmentioning

confidence: 99%

Quasi zenith satellite system-reflectometry for sea-level measurement and implication of machine learning methodology

Ansari¹,

Seok

Jamjareegulgarn

2022

Sci Rep

Self Cite

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“…To accurately analyze the regional or global tide gauge patterns that change abruptly, there is a need to apply a new class of function such as wavelet transformation, which is well localized in time and frequency. Wavelets present rapid decaying waves such as finite duration oscillations with zero mean, different than the sine waves which range infinitely (Ansari and Bae 2021). Low and high frequencies can be easily examined by using the continuous wavelet transform (CWT), which presents valuable indications concerning the time and frequency components.…”

Section: Implication Of Wavelet Analysismentioning

confidence: 99%

Modelling and mitigation of real-time sea level measurement over the coastal area of Japan

Ansari

Bae

2021

Mar Geophys Res

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This study investigates the real-time sea-level measurement from six tide gauge stations in the coastal area of Japan. Initially, the six co-located global navigation satellite station sites vertical velocity has been observed followed by estimation of the relative tide gauge velocities. The results showed that the estimated relative sea-level changes abruptly at different sites depending on the location of tide gauge stations. To accurately analyse the regional tide gauge patterns at each site, a continuous wavelet transforms based on Morse wavelet as a default transformation has been applied. Analysis showed that the peaks of tide gauges have not occurred at a static time at each site, rather they were repeating with the variation of time. The width of the spectrum peaks varied with different sites and the overall spectrum frequency was not unique. These characteristics variation with time may be due to the variables that affect the stability of the sea level measurements. Finally, the kernel extreme learning machine (KELM) approach based on variational mode decomposition is used to reconstruct the tide gauge measurements. The correlation coefficients between observed and reconstructed data sets are very high varying from 0.91 to 0.96. The estimated root mean square error between two data sets is varying from 0.04 to 0.06 m at the entire region. This means that the KELM reconstructed tide gauge showed good agreement with the observed tidal waveforms. Thus, it can be concluded that the proposed techniques can be implemented for effective real-time sea-level monitoring changes with the available data.

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“…Dai et al applied empirical mode decomposition (EMD) in denoising coordinate sequences of short GPS baselines [ 23 ]. Ansari and Bae proposed a wavelet and power spectrum analysis method for GNSS multipath error modeling and mitigation [ 24 ].…”

Section: Introductionmentioning

confidence: 99%

BDS Dual-Frequency Carrier Phase Multipath Hemispherical Map Model and Its Application in Real-Time Deformation Monitoring

Sun

Zhang

Gào

et al. 2023

Sensors

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The BDS multipath delay error is highly related to the surrounding monitoring environment, which cannot be eliminated or mitigated by applying the double difference observation model. In the actual monitoring environment, due to the complexity of the BDS constellation, it is difficult for existing algorithms to consider GEO, IGSO, MEO and other different orbital types of satellites for real-time and efficient multipath error reduction. Therefore, we propose a novel BDS dual-frequency multipath error reduction method for real deformation monitoring for BDS considering various satellite orbit types. This method extracts the single error residual of each satellite based on the assumption of “zero mean” and divides the appropriate grid density of GEO and IGSO/MEO, respectively, to construct a dual-frequency multipath hemispherical map model suitable for BDS satellites with different orbital types. This method can realize the multipath error elimination of the observed values of different orbits and different frequencies. The results of simulation experiments and real deformation monitoring data demonstrate that this method can effectively eliminate low-frequency multipath delay errors in the observation domain and coordinate domain. After multipath correction, the precision of the horizontal coordinates and height coordinates are 1.7 mm and 4.6 mm. The precision of the horizontal coordinate and height coordinate is increased by 50% and 60%, respectively. The fixed rate of ambiguity increased by 5–7%.

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Wavelet and power spectrum analysis of global navigation satellite system multipath error modeling and mitigation

Cited by 9 publications

References 80 publications

Quasi zenith satellite system-reflectometry for sea-level measurement and implication of machine learning methodology

Quasi zenith satellite system-reflectometry for sea-level measurement and implication of machine learning methodology

Modelling and mitigation of real-time sea level measurement over the coastal area of Japan

BDS Dual-Frequency Carrier Phase Multipath Hemispherical Map Model and Its Application in Real-Time Deformation Monitoring

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