Corrigendum to “Analysis of the short-term temporal variation of differential code bias in GNSS receiver” [Measurement 153 (2020) 107448]

Measurement ◽  
2020 ◽  
Vol 166 ◽  
pp. 108219
Author(s):  
Ang Liu ◽  
Zishen Li ◽  
Ningbo Wang ◽  
Chao Yuan ◽  
Hong Yuan
Keyword(s):  
Temporal Variation ◽  
Short Term ◽  
Differential Code Bias ◽  
Gnss Receiver ◽  
Code Bias

Analysis of the short-term temporal variation of differential code bias in GNSS receiver

Measurement ◽  
2020 ◽  
Vol 153 ◽  
pp. 107448 ◽  
Author(s):  
Ang Liu ◽  
Zishen Li ◽  
Ningbo Wang ◽  
Chao Yuan ◽  
Hong Yuan
Keyword(s):  
Temporal Variation ◽  
Short Term ◽  
Differential Code Bias ◽  
Gnss Receiver ◽  
Code Bias

scholarly journals MGR-DCB: A Precise Model for Multi-Constellation GNSS Receiver Differential Code Bias

2015 ◽  
Vol 69 (4) ◽  
pp. 698-708 ◽  
Author(s):  
Mohamed Abdelazeem ◽  
Rahmi N. Çelik ◽  
Ahmed El-Rabbany
Keyword(s):  
Total Electron Content ◽  
Satellite System ◽  
Mean Difference ◽  
Electron Content ◽  
Vertical Total Electron Content ◽  
International Gnss Service ◽  
Total Electron ◽  
Differential Code Bias ◽  
Gnss Receiver ◽  
Code Bias

In this study, we develop a Multi-constellation Global Navigation Satellite System (GNSS) Receiver Differential Code Bias (MGR-DCB) model. The model estimates the receiver DCBs for the Global Positioning System (GPS), BeiDou and Galileo signals from the ionosphere-corrected geometry-free linear combinations of the code observations. In order to account for the ionospheric delay, a Regional Ionospheric Model (RIM) over Europe is developed. GPS observations from 60 International GNSS Servoce (IGS) and EUREF reference stations are processed in the Bernese-5·2 Precise Point Positioning (PPP) module to estimate the Vertical Total Electron Content (VTEC). The RIM has spatial and temporal resolutions of 1° × 1° and 15 minutes, respectively. The receiver DCBs for three stations from the International GNSS Service Multi-GNSS Experiment (IGS-MGEX) are estimated for three different days. The estimated DCBs are compared with the MGEX published values. The results show agreement with the MGEX values with mean difference and Root Mean Square Error (RMSE) values less than 1 ns. In addition, the combined GPS, BeiDou and Galileo VTEC values are evaluated and compared with the IGS Global Ionospheric Maps (IGS-GIM) counterparts. The results show agreement with the GIM values with mean difference and RMSE values less than 1 Total Electron Content Unit (TECU).

scholarly journals The Effect of BDS-3 Time Group Delay and Differential Code Bias Corrections on Positioning

2020 ◽  
Vol 11 (1) ◽  
pp. 104
Author(s):  
Peipei Dai ◽  
Jianping Xing ◽  
Yulong Ge ◽  
Xuhai Yang ◽  
Weijin Qin ◽  
...  
Keyword(s):  
Group Delay ◽  
Assessment System ◽  
Convergence Time ◽  
Single Frequency ◽  
Positioning Accuracy ◽  
Differential Code Bias ◽  
Receiver Clock ◽  
Code Bias ◽  
Point Positioning ◽  
The Impact

The timing group delay parameter (TGD) or differential code bias parameter (DCB) is an important factor that affects the performance of GNSS basic services; therefore, TGD and DCB must be taken seriously. Moreover, the TGD parameter is modulated in the navigation message, taking into account the impact of TGD on the performance of the basic service. International GNSS Monitoring and Assessment System (iGMAS) provides the broadcast ephemeris with TGD parameter and the Chinese Academy of Science (CAS) provides DCB products. In this paper, the current available BDS-3 TGD and DCB parameters are firstly described in detail, and the relationship of TGD and DCB for BDS-3 is figured out. Then, correction models of BDS-3 TGD and DCB in standard point positioning (SPP) or precise point positioning (PPP) are given, which can be applied in various situations. For the effects of TGD and DCB in the SPP and PPP solution processes, all the signals from BDS-3 were researched, and the validity of TGD and DCB has been further verified. The experimental results show that the accuracy of B1I, B1C and B2a single-frequency SPP with TGD or DCB correction was improved by approximately 12–60%. TGD will not be considered for B3I single-frequency, because the broadcast satellite clock offset is based on the B3I as the reference signal. The positioning accuracy of B1I/B3I and B1C/B2a dual-frequency SPP showed that the improvement range for horizontal components is 60.2% to 74.4%, and the vertical components improved by about 50% after the modification of TGD and DCB. In addition, most of the uncorrected code biases are mostly absorbed into the receiver clock bias and other parameters for PPP, resulting in longer convergence time. The convergence time can be max increased by up to 50% when the DCB parameters are corrected. Consequently, the positioning accuracy can reach the centimeter level after convergence, but it is critical for PPP convergence time and receiver clock bias that the TGD and DCB correction be considered seriously.

Performance Analysis of GPS/BDS Precise Point Positioning

2015 ◽  
Vol 713-715 ◽  
pp. 1123-1126
Author(s):  
Xiao Yu Li ◽  
Jun Wang ◽  
Ya Tao Liu
Keyword(s):  
Precise Point Positioning ◽  
Satellite Orbit ◽  
Gps Measurements ◽  
Differential Code Bias ◽  
Phase Offset ◽  
Antenna Phase ◽  
Offset Correction ◽  
Code Bias ◽  
Point Positioning ◽  
Processing Steps

Precise Point Positioning (PPP) with GPS measurements has achieved a level of success. In order to benefit from the multiple available constellations, research has been undertaken to combineGPS and BDS measurements in PPP processing.Mathematical models of GPS/BDS combined precise point positioning are introduced in this paper. GPS/BDS combined PPP models are developed based on the GPS-only PPP. The data pre-processing steps include applying satellite orbit and clock corrections, satellite antenna phase offset correction, receiver antenna phase offset correction, differential code bias corrections, troposphere delay corrections and the the Ionosphere-free observation combination is used. The results show that the positioning precision and convergence speed of GPS/BDS combined PPP are improved compared with GPS-only PPP.

scholarly journals Assessment of BeiDou differential code bias variations from multi-GNSS network observations

2016 ◽  
Vol 34 (2) ◽  
pp. 259-269 ◽  
Author(s):  
S. G. Jin ◽  
R. Jin ◽  
D. Li
Keyword(s):  
Orbital Plane ◽  
Satellite Orbit ◽  
Satellite System ◽  
Global Navigation Satellite Systems ◽  
Dominant Factor ◽  
Earth Orbit ◽  
Navigation Satellite ◽  
Differential Code Bias ◽  
Code Bias ◽  
Gnss Network

Abstract. The differential code bias (DCB) of global navigation satellite systems (GNSSs) affects precise ionospheric modeling and applications. In this paper, daily DCBs of the BeiDou Navigation Satellite System (BDS) are estimated and investigated from 2-year multi-GNSS network observations (2013–2014) based on global ionospheric maps (GIMs) from the Center for Orbit Determination in Europe (CODE), which are compared with Global Positioning System (GPS) results. The DCB of BDS satellites is a little less stable than GPS solutions, especially for geostationary Earth orbit (GEO) satellites. The BDS GEO observations decrease the precision of inclined geosynchronous satellite orbit (IGSO) and medium Earth orbit (MEO) DCB estimations. The RMS of BDS satellites DCB decreases to about 0.2 ns when we remove BDS GEO observations. Zero-mean condition effects are not the dominant factor for the higher RMS of BDS satellites DCB. Although there are no obvious secular variations in the DCB time series, sub-nanosecond variations are visible for both BDS and GPS satellites DCBs during 2013–2014. For satellites in the same orbital plane, their DCB variations have similar characteristics. In addition, variations in receivers DCB in the same region are found with a similar pattern between BDS and GPS. These variations in both GPS and BDS DCBs are mainly related to the estimated error from ionospheric variability, while the BDS DCB intrinsic variation is in sub-nanoseconds.

Short-term growth (RNA/DNA ratio) of yellow perch (Perca flavescens) in relation to environmental influences and spatio-temporal variation in a shallow fluvial lake

2007 ◽  
Vol 64 (12) ◽  
pp. 1646-1655 ◽  
Author(s):  
Hélène Glémet ◽  
Marco A Rodríguez
Keyword(s):  
Temporal Variation ◽  
Water Level ◽  
Yellow Perch ◽  
Perca Flavescens ◽  
Negative Influence ◽  
Water Levels ◽  
Short Term ◽  
Ecological Processes ◽  
Flood Pulse Concept ◽  
Spatio Temporal

Shallow fluvial lakes are heterogeneous ecosystems in which marked spatio-temporal variation renders difficult the analysis of key ecological processes, such as growth. In this study, we used generalized additive modelling of the RNA/DNA ratio, an index of short-term growth, to investigate the influence of environmental variables and spatio-temporal variation on growth of yellow perch (Perca flavescens) in Lake St. Pierre, Quebec, Canada. Temperature and water level had seemingly stronger effects on short-term growth than seasonal change or spatial variation between and along the lakeshores. Consistent with previous studies, the maximum RNA/DNA ratio was found at 20.5 °C, suggesting that our approach provides a useful tool for estimating thermal optima for growth in the field. The RNA/DNA ratio showed a positive relationship with water level, as predicted by the flood pulse concept, a finding with implications for ecosystem productivity in fluvial lakes. The RNA/DNA ratio was more variable along the north than the south shore, possibly reflecting exposure to more differentiated water masses. The negative influence of both high temperatures and low water levels on growth points to potential impacts of climatic change on fish production in shallow fluvial lakes.

Influence of the GLONASS inter-frequency bias on differential code bias estimation and ionospheric modeling

GPS Solutions ◽  
2017 ◽  
Vol 21 (3) ◽  
pp. 1355-1367 ◽  
Author(s):  
Xiaohong Zhang ◽  
Weiliang Xie ◽  
Xiaodong Ren ◽  
Xingxing Li ◽  
Keke Zhang ◽  
...  
Keyword(s):  
Bias Estimation ◽  
Differential Code Bias ◽  
Ionospheric Modeling ◽  
Code Bias
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