Enhanced multipath mitigation method based on multi-resolution CNR model and adaptive statistical test strategy for real-time kinematic PPP

Su, Mingkun; Yang, Ya‐Nan; Qiao, Lei; Teng, Xuyang; Song, Huina

doi:10.1016/j.asr.2020.10.035

Cited by 12 publications

(6 citation statements)

References 36 publications

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“…( 2) Mitigate the random noise by using the wavelet transform denoise method (for the concrete method, refer to Su et al [26]). ( 3) Extract the fitted CNR from the denoised original CNR by applying a third-order polynomial fitting method (for the method, refer to Su et al [18]). ( 4) Select the appropriate original denoised and fitted CNR by threshold or elevation angle.…”

Section: Summary Of the Proposed Methodsmentioning

confidence: 99%

“…Furthermore, based on the relationship between the CNR, direct signal, and indirect signal, the amplitude of the received signal can be reflected in the CNR. Considering that the CNR is positively related to the amplitude of the signal, the received GPS signal can be further modeled as follows [18]:…”

Section: Relationship Between Multipath and Cnrmentioning

confidence: 99%

“…Zhang et al [17] demonstrated an improved method to mitigate the influence of carrier phase multipath error based on CNR statistical strategies. In addition, Su et al [18] indicated that the multipath error of carrier phase observation is strongly related to the CNR, and there is an inverse relation between them. Tian et al [19] investigated the influence of marine environments on BDS satellites, and experimental results show that the CNR of BDS satellites collected in a marine environment is lower than in a land environment.…”

Section: Introductionmentioning

confidence: 99%

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Accurate Retrieval of the Whole Flood Process from Occurrence to Recession Based on GPS Original CNR, Fitted CNR, and Seamless CNR Series

Tong

Zheng

et al. 2023

Remote Sensing

Self Cite

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The CNR (Carrier-to-Noise Ratio) of GPS (Global Positioning System) satellites is highly relevant to the multipath error. The multipath error is more serious in the flood environment since the reflection and diffraction coefficients of water are much higher compared to dry soil. Thus, the amplitude of CNR will decrease in the flood environment. In this study, the relationship between multipath error, flooding, and CNR is introduced in theory. Then, by using the characteristic of the orbital repetition period, the stability of CNR between 2 adjacent days in a static observation environment is demonstrated by 32 MGEX (Multi-GNSS Experiment) stations in different latitude and longitude regions of the world. The results show that the average RMS of different CNRs between two adjacent days is only about 0.62 dB-Hz. In addition, the correlation coefficient of CNRs between two adjacent days is analyzed. The correlation coefficient of the original signal CNR is 0.997. Moreover, after mitigating the influence of random noise and lower CNR, the correlation coefficients of the fitted CNRs larger than 40 dB-Hz can reach 0.999. Thus, based on the fluctuation in original CNR, fitted CNR, and seamless series characteristics of CNR, the whole flood process from occurrence to recession can be retrieved. A flood that occurred in Zhengzhou City, China, from DOY 200 to DOY 202, 2021 is used to demonstrate the process of retrieval. The experimental results indicate that the flood appeared at about 15:30 pm on DOY 200, reached a peak at approximately 8:30 am on DOY 202, and totally subsided at about 10:00 am on DOY 202. In conclusion, the CNR can be effectively used to retrieve the whole process of the flood, which lays a foundation for researching flood detection and warning based on GPS satellites.

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Section: Summary Of the Proposed Methodsmentioning

confidence: 99%

Section: Relationship Between Multipath and Cnrmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Accurate Retrieval of the Whole Flood Process from Occurrence to Recession Based on GPS Original CNR, Fitted CNR, and Seamless CNR Series

Tong

Zheng

et al. 2023

Remote Sensing

Self Cite

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“…In recent years, with the continuous improvement of GNSS theory, many analyses and evaluations of GNSS observations quality based on single indicator algorithms are studied by many scholars (Liu et al 2020). In their study, GNSS observations quality assessment is mostly conducted based on single indicators, such as pseudorange multipath (Su et al 2020, Yuan et al 2021, CNR (He et al 2020), and Cycle slip detection (Chang et al 2018, Li et al 2018, Yao et al 2020. However, the observation quality cannot be comprehensively reflected by a single index, and the studies about GNSS observations assessment based on multiple indicators are few.…”

Section: Introductionmentioning

confidence: 99%

Comprehensive classification assessment of GNSS observation data quality by fusing k-means and KNN algorithms

Li,

Huang,

Wang

et al. 2023

GPS Solut

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The observation data is the basis for the global navigation satellite system (GNSS) to provide positioning, navigation and timing (PNT) service, and the observation quality directly determines the performance level of PNT service. At present, the analysis of GNSS observations quality is partial and can only be based on a single index assessment. GNSS observation quality is difficult to analyze comprehensively by fusing multiple indicators. To solve the above problem, the supervised and unsupervised machine learning algorithms are applied, and a new comprehensive and classification method of GNSS observations quality based on the k-means clustering algorithm (k-means) and K-nearest neighbor algorithm (KNN) was proposed. The four core index features of GNSS observations, including data integrity rate, carrier-to-noise-density ratio (CNR), pseudorange multipath and the number of observations per slip, were selected to construct the sample dataset. The sample set was unsupervised clustered based on the k-means algorithm, and the classification label of GNSS observations quality was obtained. Then KNN algorithm was used to construct a comprehensive classification and evaluation model for GNSS observations quality. The data of 30 MGEX stations in the Asia-Pacific region in 2019 were selected for modeling analysis. The experiment results show that: (1) a strong correlation has been revealed between pseudorange multipath, CNR and the number of observations per slip. (2) The average classification correctness rate of the new model was over 90% by 𝑛-fold cross-validation. (3) The new model can effectively realize the automatic evaluation and classification of GNSS observations quality and easily distinguish the superiority and inferiority of the station observations. The relevant results provide a new idea for the automatic classification and assessment of GNSS observation quality.

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“…Signal-to-noise ratio (SNR) measured by GNSS receivers can be used for multipath characterization or observation weighting (Bilich et al, 2008;Su et al, 2021). However, its e ciency is dependent on the SNR data quality and antenna gain pattern.…”

Section: Introductionmentioning

confidence: 99%

Machine learning-based multipath modelling in spatial-domain: a demonstration on GNSS short baseline processing

Pan

Möller

Soja

2023

Preprint

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Multipath is the main unmodeled error source hindering high-precision Global Navigation Satellite System (GNSS) data processing. Conventional multipath mitigation methods, such as sidereal filtering (SF) and multipath hemispherical map (MHM), have certain disadvantages: they are either too complicated for implementation or not effective enough for multipath mitigation. In this study, we propose a machine learning (ML)-based multipath mitigation method. Multipath modelling was formulated as a regression task, and the multipath errors were fitted with respect to azimuth and elevation in the spatial-domain. We collected 30 days of 1 Hz GPS data to validate the proposed method. In total five short baselines were formed and multipath errors were extracted from the posfit residuals. ML-based multipath models, as well as observation-domain SF and MHM models, were constructed using 5 days of residuals before the target day and later applied for multipath correction. It was found that the XGBoost (XGB) method outperformed SF and MHM. It achieved the highest residual reduction rates, which were 24.9%, 36.2%, 25.5% and 20.4% for GPS P1, P2, L1 and L2 observations, respectively. After applying the XGB-based multipath corrections, kinematic positioning precisions of 1.6 mm, 1.9 mm and 4.5 mm could be achieved in east, north and up components, respectively, corresponding to 20.0%, 17.4% and 16.7% improvements compared to the original solutions. The effectiveness of the ML-based multipath model was further validated using 30 s sampling data. We conclude that the ML-based multipath mitigation method is effective, easy-to-use, and can be easily extended by adding auxiliary input features, such as signal-to-noise ratio (SNR), during model training.

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Enhanced multipath mitigation method based on multi-resolution CNR model and adaptive statistical test strategy for real-time kinematic PPP

Cited by 12 publications

References 36 publications

Accurate Retrieval of the Whole Flood Process from Occurrence to Recession Based on GPS Original CNR, Fitted CNR, and Seamless CNR Series

Accurate Retrieval of the Whole Flood Process from Occurrence to Recession Based on GPS Original CNR, Fitted CNR, and Seamless CNR Series

Comprehensive classification assessment of GNSS observation data quality by fusing k-means and KNN algorithms

Machine learning-based multipath modelling in spatial-domain: a demonstration on GNSS short baseline processing

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