An improved time-domain multipath mitigation method based on the constraint of satellite elevation for low-cost single frequency receiver

Su, Mingkun; Feng, Weijun; Qiao, Lei; Qiu, Zhaoyang; Zhang, Huan; Zheng, Jiansheng; Yang, Ya‐Nan

doi:10.1016/j.asr.2022.02.043

Cited by 6 publications

(2 citation statements)

References 33 publications

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“…The reason for this phenomenon is that the elevation angle of these two stages is low, and the multipath error and random noise perform more seriously than that at high elevation. This result can also be found in Su et al [28]. Moreover, it should be noted that the same phenomenon can also be found on other satellites.…”

Section: Influence Of Rainfall On L4supporting

confidence: 85%

Analysis of the Influence of Flood on the L4 Combination Observation of GPS and GLONASS Satellites

Gong

et al. 2023

Atmosphere

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With global warming, extreme weather such as floods and waterlogging occurs more frequently and seriously in recent years. During the flood, the surrounding environment of the GNSS (Global Navigation Satellite System) station will change as the volume of water increases. Considering the multipath error is directly relevant to the observation environment, thus, the influence of flood on the L4 combination observation (a geometry-free ionosphere-free linear combination of carrier phase) which is related to the multipath error of GPS (Global Positioning System) and GLONASS satellites is investigated in depth. In addition, the ground track repetition periods of GPS and GLONASS satellites are analyzed in the sky plot to illustrate the rationality of chosen reference day. Based on the results of the satellite sky plot, one and eight days are adopted to demonstrate the influence of flood on L4 combination observation for GPS and GLONASS satellites, respectively. Real data sets collected at the ZHNZ GNSS observation station during the flood from DOY (Day of Year) 193 to DOY 204, 2021 are used. Experimental results show that the flood has a significant impact on the L4 combination observation of GPS and GLONASS satellites, and the fluctuation of L4 under flood performs much larger than that of without flood. For GPS satellites, the maximum RMS (root mean square) increase rate of L4 under flood is approximately 186.67% on the G31 satellite. Even for the minimum RMS increase rate, it can reach approximately 23.52%, which is the G02 satellite. Moreover, the average RMS increase rate of GPS and GLONASS satellites can reach approximately 109.53% and 43.65%, respectively. In addition, the influence of rainfall and hardware device are also investigated, which can further demonstrate that the fluctuation of L4 is mainly caused by the flood but not by the rainfall and hardware device elements. Thus, based on the above results, the influence of flood on L4 observation should be taken into account during the applications of L4 used, such as the retrieval of soil moisture and vegetation water content based on GNSS L4 combination observations

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Section: Influence Of Rainfall On L4supporting

confidence: 85%

Analysis of the Influence of Flood on the L4 Combination Observation of GPS and GLONASS Satellites

Gong

et al. 2023

Atmosphere

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“…Lau and Lawrence [66] proposed a general and robust three-stage wavelet denoising method for repetitive time-based carrier-phase multipath filtering in relative positioning. Su et al [67] used the satellite elevation as the constraint information of the wavelet transform to extract an accurate multipath error model and then used SF for multipath error correction.…”

Section: Multipath Error Mitigation Methods Based On the Observation ...mentioning

confidence: 99%

GNSS Carrier-Phase Multipath Modeling and Correction: A Review and Prospect of Data Processing Methods

Zhang,

Sun

et al. 2024

Remote Sensing

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A multipath error is one of the main sources of GNSS positioning errors. It cannot be eliminated by forming double-difference and other methods, and it has become an issue in GNSS positioning error processing, because it is mainly related to the surrounding environment of the station. To address multipath errors, three main mitigation strategies are employed: site selection, hardware enhancements, and data processing. Among these, data processing methods have been a focal point of research due to their cost-effectiveness, impressive performance, and widespread applicability. This paper focuses on the review of data processing mitigation methods for GNSS carrier-phase multipath errors. The paper begins by elucidating the origins and mitigation strategies of multipath errors. Subsequently, it reviews the current research status pertaining to data processing methods using stochastic and functional models to counter multipath errors. The paper also provides an overview of filtering techniques for extracting multipath error models from coordinate sequences or observations. Additionally, it introduces the evolution and algorithmic workflow of sidereal filtering (SF) and multipath hemispherical mapping (MHM), from both coordinate and observation domain perspectives. Furthermore, the paper emphasizes the practical significance and research relevance of multipath error processing. It concludes by delineating future research directions in the realm of multipath error mitigation.

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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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An improved time-domain multipath mitigation method based on the constraint of satellite elevation for low-cost single frequency receiver

Cited by 6 publications

References 33 publications

Analysis of the Influence of Flood on the L4 Combination Observation of GPS and GLONASS Satellites

Analysis of the Influence of Flood on the L4 Combination Observation of GPS and GLONASS Satellites

GNSS Carrier-Phase Multipath Modeling and Correction: A Review and Prospect of Data Processing Methods

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

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