New range domain carrier‐smoothed code filtering with dual‐frequency BDS data

Chen, Chao; Chang, Guobin; Zhang, Shubi; Chen, Guoliang; Luo, Fei

doi:10.1002/asjc.1996

Cited by 4 publications

(2 citation statements)

References 33 publications

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“…CSC, also known as Hatch filtering, reduces the noise in the pseudorange without resolving the ambiguities [4]. In fact, it uses the delta range to obtain a predicted pseudorange value, which is then weighted by the average of the predicted value and the original pseudorange observation [5][6][7]. The delta range is derived from the carrier observations of consecutive epochs.…”

Section: Introductionmentioning

confidence: 99%

An Improved Carrier-Smoothing Code Algorithm for BDS Satellites with SICB

Zhang,

Ma,

Gao

et al. 2023

Remote Sensing

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Carrier Smoothing Code (CSC), as a low-pass filter, has been widely used in GNSS positioning processing to reduce pseudorange noise via carrier phases. However, current CSC methods do not consider the systematic bias between the code and carrier phase observation, also known as Satellite-induced Code Bias (SICB). SICB has been identified in the BDS-2 and the bias will reduce the accuracy or reliability of the CSC. To confront bias, an improved CSC algorithm is proposed by considering SICB for GEO, IGSO, and MEO satellites in BDS constellations. The correction model of SICB for IGSO/MEO satellites is established by using a 0.1-degree interval piecewise weighted least squares Third-order Curve Fitting Method (TOCFM). The Variational Mode Decomposition combined with Wavelet Transform (VMD-WT) is proposed to establish the correction model of SICB for the GEO satellite. To verify the proposed method, the SICB model was established by collecting 30 Multi-GNSS Experiment (MGEX) BDS stations in different seasons of a year, in which the BDS data of ALIC, KRGG, KOUR, GCGO, GAMG, and SGOC stations were selected for 11 consecutive days to verify the effectiveness of the algorithm. The results show that there is obvious SICB in the BDS-2 Multipath (MP) combination, but the SICB in the BDS-3 MP is smaller and can be ignored. Compared with the modeling in the references, TOCFM is more suitable for IGSO/MEO SICB modeling, especially for the SICB correction at low elevation angles. After the VMD-WT correction, the Root Mean Square Error (RMSE) of SICB of B1I, B2I, and B3I in GEO satellites is reduced by 53.35%, 63.50%, and 64.71% respectively. Moreover, we carried out ionosphere-free Single Point Positioning (IF SPP), Ionosphere-free CSC SPP (IF CSC SPP), CSC single point positioning with the IGSO/MEO SICB Correction based on the TOCFA Method (IGSO/MEO SICB CSC), and CSC single point positioning with the IGSO/MEO/GEO SICB correction based on VMD-WT and TOCFA (IGSO/MEO/GEO SICB CSC), respectively. Compared to IF SPP, the average improvement of the IGSO/MEO/GEO SICB CSC algorithm in the north, east, and up directions was 24.42%, 27.94%, and 24.98%, respectively, and the average reduction in 3D RMSE is 24.54%. Compared with IF CSC SPP, the average improvement of IGSO/MEO/GEO SICB CSC is 7.03%, 6.50%, and 10.48% in the north, east, and up directions, respectively, while the average reduction in 3D RMSE was 9.86%. IGSO/MEO SICB mainly improves the U direction positioning accuracy, and GEO SICB mainly improves the E and U direction positioning accuracy. After the IGSO/MEO/GEO SICB correction, the overall improvement was about 10% and positioning improved to a certain extent.

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Section: Introductionmentioning

confidence: 99%

An Improved Carrier-Smoothing Code Algorithm for BDS Satellites with SICB

Zhang,

Ma,

Gao

et al. 2023

Remote Sensing

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“…They obtained a higher precision smoothed pseudorange than Hatch's smoother by employing the recursive LS, which is only used for an ionospheric removed GNSS. To counteract the influence of ionospheric delay and improve the precision of smoothed pseudorange, Chen [25] et al exploited a similar solution to the recursive LS [24] to integrate the pseudorange and TDCP (Time Difference Carrier Phase), which divided GNSS measurements into dispersion and non-dispersion term. Then, they exposed the merit according to the residual error and positioning accuracy, which lacked analyzing convergence speed and precision, and application scope.…”

Section: Introductionmentioning

confidence: 99%

A Novel Dynamical Filter Based on Multi-Epochs Least-Squares to Integrate the Carrier Phase and Pseudorange Observation for GNSS Measurement

Gao

Psimoulis

et al. 2020

Remote Sensing

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The high noise of pseudorange and the ambiguity of carrier phase observation restrain the GNSS (Global Navigation Satellite System) application in military, industrial, and agricultural, to name a few. Thus, it is crucial for GNSS technology to integrate the pseudorange and carrier phase observations. However, the traditional method proposed by Hatch has obtained only a low convergence speed and precision. For higher convergence speed and precision of the smoothed pseudorange, aiming to improve positioning accuracy and expand the application of GNSS, we introduced a new method named MELS (Multi-Epochs Least-Squares) that considered the cross-correlation of the estimating parameters inspired by DELS (Double-Epochs Least-Square). In this study, the ionospheric delay was compensated, and so its impact was limited to the performance of the filters, and then exploited the various filters to integrate carrier phase observation and pseudorange. We compared the various types of Hatch’s filter and LS (Least-Square) methods using simulation datasets, which confirmed that the types of LS method provided a smaller residual error and a faster convergence speed than Hatch’s method under various precisions of raw pseudorange. The experimental results from the measured GNSS data showed that LS methods provided better performance than Hatch’s methods at E and U directions and a lower accuracy at N direction. Nevertheless, the types of LS method and Hatch’s methods improved about 12% and 9–10% at the 3D direction, respectively, which illustrated the accumulating improvement at the enhanced directions was more than the decreased direction, proving that the types of LS method resulted to better performance than the Hatch’s filters. Additionally, the curve of residual and precision based on various LS methods illustrated that the MELS only provided a millimeter accuracy difference compared with DELS, which was proved by the simulated and measured GNSS datasets.

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