High-Accuracy Attitude Determination Using Single-Difference Observables Based on Multi-Antenna GNSS Receiver with a Common Clock

A global navigation satellite system (GNSS) receiver with multi-antenna using clock synchronization technology is a powerful piece of equipment for precise attitude determination and reducing costs. The single-difference (SD) can eliminate both the satellites and receiver clock errors with the common clock between antennas, which benefits the GNSS short-baseline attitude determination due to its lower noise, higher redundancy and stronger function model strength. However, the existence of uncalibrated phase delay (UPD) makes it difficult to obtain fixed SD attitude solutions. Therefore, the key problem for the fixed SD attitude solutions is to separate the SD UPD and fix the SD ambiguities into integers between antennas. This article introduces the one-step ambiguity substitution approach to separate the SD UPD, through which we merge the SD UPD parameter with the SD ambiguity of the reference satellite ambiguity as the new SD UPD parameter. Reconstructing the other SD ambiguities, the rank deficiency can be remedied by nature, and the new SD ambiguities can have a natural integer feature. Finally, the fixed SD baseline and attitude solutions are obtained by combining the ambiguity substitution approach with integer ambiguity resolution (IAR). To verify the effect of the ambiguity substitution approach and the advantages of the SD observables with a common clock in practical applications, we conducted static, kinematic, and vehicle experiments. In static experiments, the root mean squared errors (RMSEs) of the yaw and pitch angles obtained by the SD observables with a common clock were improved by approximately 80% and 93%, respectively, compared to double-difference (DD) observables with a common clock in multi-day attitude solutions. The kinematic results show that the dispersion of the SD-Fix in the pitch angle is two times less that of the DD-Fix, and the standard deviations (STDs) of the pitch angle for SD-Fix can reach 0.02°. Based on the feasibility, five bridges with low pitch angles in the vehicle experiment environment, which the DD observables cannot detect, were detected by the SD observables with a common clock. The attitude angles obtained by the SD observables were also consistent with the fiber optic gyroscope (FOG) inertial navigation system (INS). This research on the SD observables with a common clock provides higher accuracy.

Whole Genome Sequencing of SARS-CoV-2: Assessment of the Ion Torrent AmpliSeq Panel and Comparison with the Illumina-MiSeq ARTIC Protocol

Background: Fast and effective methods are needed for sequencing the SARS-CoV-2 genome to track genetic mutations and identify new and emerging variants during the ongoing pandemic. Objective: Assess the performance of the SARS-CoV-2 AmpliSeq Research Panel and S5 plug-in analysis tools for whole genome sequence analysis of SARS-CoV-2 and compare the results to those obtained with the MiSeq based ARTIC analysis pipeline, using metrics such as depth, coverage, and concordance of single nucleotide variant (SNV) calls. Methods: A total of 191 clinical specimens and a single cultured isolate were extracted and sequenced with AmpliSeq technology and analysis tools. Of the 191 clinical specimens, 83 (Ct 15.58 – 32.54) were also sequenced using an Illumina MiSeq based method with the ARTIC analysis pipeline for a direct comparison. Results: 176 of the 191 clinical specimens sequenced on the S5XL and prepared using the SARS-CoV-2 Research Panel, had near complete coverage (>98%) of the viral genome, with an average depth of 5031x. Similar coverage (>98%) levels were observed for 81/83 primary specimens sequenced with both methods tested. The sample with the lowest viral load (Ct of 32.54) achieved 89% coverage using the MiSeq method and failed to sequence with the AmpliSeq method. Consensus sequences produced by each method were identical in 81/82 samples, in areas of equal coverage, with a single difference present in one sample. Conclusions: The AmpliSeq approach is as effective as the Illumina based method using ARTIC V3 amplification for sequencing SARS-CoV-2 direct from patient specimens across a range of viral loads (Ct 15.56-32.54, median = 22.18). The AmpliSeq workflow is very easily automated with the Ion Chef and S5 instruments and requires less training and experience with NGS preparation than the Illumina workflow.

Estimating the Fractional Cycle Biases for GPS Triple-Frequency Precise Point Positioning with Ambiguity Resolution Based on IGS Ultra-Rapid Predicted Orbits

We investigate the estimation of the fractional cycle biases (FCBs) for GPS triple-frequency uncombined precise point positioning (PPP) with ambiguity resolution (AR) based on the IGS ultra-rapid predicted (IGU) orbits. The impact of the IGU orbit errors on the performance of GPS triple-frequency PPP AR is also assessed. The extra-wide-lane (EWL), wide-lane (WL) and narrow-lane (NL) FCBs are generated with the single difference (SD) between satellites model using the global reference stations based on the IGU orbits. For comparison purposes, the EWL, WL and NL FCBs based on the IGS final precise (IGF) orbits are estimated. Each of the EWL, WL and NL FCBs based on IGF and IGU orbits are converted to the uncombined FCBs to implement the static and kinematic triple-frequency PPP AR. Due to the short wavelengths of NL ambiguities, the IGU orbit errors significantly impact the precision and stability of NL FCBs. An average STD of 0.033 cycles is achieved for the NL FCBs based on IGF orbits, while the value of the NL FCBs based on IGU orbits is 0.133 cycles. In contrast, the EWL and WL FCBs generated based on IGU orbits have comparable precision and stability to those generated based on IGF orbits. The use of IGU orbits results in an increased time-to-first-fix (TTFF) and lower fixing rates compared to the use of IGF orbits. Average TTFFs of 23.3 min (static) and 31.1 min (kinematic) and fixing rates of 98.1% (static) and 97.4% (kinematic) are achieved for the triple-frequency PPP AR based on IGF orbits. The average TTFFs increase to 27.0 min (static) and 37.9 min (kinematic) with fixing rates of 97.0% (static) and 96.3% (kinematic) based on the IGU orbits. The convergence times and positioning accuracy of PPP and PPP AR based on IGU orbits are slightly worse than those based on IGF orbits. Additionally, limited by the number of satellites transmitting three frequency signals, the introduction of the third frequency, L5, has a marginal impact on the performance of PPP and PPP AR. The GPS triple-frequency PPP AR performance is expected to improve with the deployment of new-generation satellites capable of transmitting the L5 signal.

An improved precise point positioning model using GPS and Galileo observations

Precise point positioning (PPP) allows for centimeter- to decimeter-level positioning accuracy using a single global navigation satellite system (GNSS) receiver. However, the use of PPP is presently limited due to the time required for the solution to converge or re-converge to the expected accuracy, which typically requires about 30 minutes. This relatively long convergence time is essentially caused by the existing un-modeled GNSS residual errors. Additionally, in urban areas, the number of visible satellites is usually limited when a single satellite constellation is used, which in turn slows down the PPP solution convergence. This, however, can be overcome by combining the observations of two constellations, namely the GPS and Galileo systems. Unfortunately, combining the GPS and Galileo constellations, although enhances the satellite geometry, introduces additional biases that must be considered in the observation mathematical models. These include the GPS-to-Galileo time offset, and Galileo satellite and receiver hardware delays. In addition, the stochastic characteristics of the new Galileo E1 and E5a signals must be determined to a high degree of precision. This can be done by analyzing various sets of GPS and Galileo measurements collected at two stations with short separation. Several PPP models are developed in this dissertation, which combine GPS and Galileo observations in the un-differenced and between-satellite single-difference (BSSD) modes. These include the traditional un-differenced model, the decoupled clock model, the semi-decoupled clock model, and the between-satellite single-difference model. It is shown that the traditional un-differenced GPS/Galileo PPP model, the GPS decoupled clock model, and semi-decoupled clock GPS/Galileo PPP model improve the convergence time by about 25% in comparison with the un-differenced GPS-only PPP model. In addition, the semi-decoupled GPS/Galileo PPP model improves the solution precision by about 25% compared to the traditional un-differenced GPS/Galileo PPP model. Moreover, the BSSD GPS/Galileo PPP model improves the solution convergence time by about 50%, in comparison with the un-differenced GPS PPP model, regardless of the type of BSSD combination used. As well, the BSSD model improves the solution precision by about 50% and 25% when the BSSD loose and tight combinations are used, respectively, in comparison with the un-differenced GPS-only model.

An Improved Model For Precise Point Positioning With Modernized Glogbal Positioning System

Recent developments in GPS positioning show that a user with a standalone GPS receiver can obtain positioning accuracy comparable to that of carrier-phase-based differential positioning. Such a technique is commonly known as precise point positioning (PPP). A significant challenge of PPP, however, is that it typically requires a minimum of 30 minutes to achieve centimeter- to decimeter-level accuracy. This relatively long convergence time is the result of un-modeled GPS residual errors. This thesis addresses error mitigation techniques to achieve near real-time PPP. To explore the full advantage of the modernized GPS L2C signal, it is essential to determine its stochastic characteristics and code bias. GPS measurements were collected in order to study the stochastic characteristics of the modernized GPS L2C signal. As a byproduct, the stochastic characteristics of the legacy GPS signals, namely C/A and P2 codes, were also determined and then used to verify the developed stochastic model of the modernized signal. The differential code biases between P2 and C2, DCB P2-C2, were also estimated using the Bernese GPS software. A major residual error component, which affects the convergence of PPP solution, is the higherorder ionospheric delay. We rigorously modeled the second-order ionospheric delay, which represents the bulk of higher-order ionospheric delay, for our PPP model. First, we investigated the effect of second-order ionospheric delay on GPS satellite orbit and clock corrections. Second, we used the estimated satellite orbit and clock corrections to process the GPS data from several IGS stations after correcting the data for the effect of second-order ionospheric delay. The results demonstrated an improvement of up to 25% in the precision of the estimated coordinates with the second-order ionospheric delay, as well as reduction of the convergence time of the estimated parameters by about 15%, depending on the geographic location and ionospheric and geomagnetic conditions. Between-satellite single-difference PPP algorithms were developed to cancel out the receiver clock error, receiver initial phase bias, and receiver hardware delay. The decoupled clock corrections, provided by NRCan, were also applied to account for the satellite hardware delay and satellite initial phase bias. GPS data collected from several IGS stations were processed using the un-differenced model, un-differenced decoupled clock model, between-satellite singledifference (BSSD) model, and between-satellite single-difference using the decoupled clock (BSSD-DC) model. The results showed that the proposed BSSD model significantly improved the PPP convergence time by 50% and improved the solution precision by more than 60% over the traditional un-differenced PPP model.

An Improved Model For Precise Point Positioning With Modernized Glogbal Positioning System

Recent developments in GPS positioning show that a user with a standalone GPS receiver can obtain positioning accuracy comparable to that of carrier-phase-based differential positioning. Such a technique is commonly known as precise point positioning (PPP). A significant challenge of PPP, however, is that it typically requires a minimum of 30 minutes to achieve centimeter- to decimeter-level accuracy. This relatively long convergence time is the result of un-modeled GPS residual errors. This thesis addresses error mitigation techniques to achieve near real-time PPP. To explore the full advantage of the modernized GPS L2C signal, it is essential to determine its stochastic characteristics and code bias. GPS measurements were collected in order to study the stochastic characteristics of the modernized GPS L2C signal. As a byproduct, the stochastic characteristics of the legacy GPS signals, namely C/A and P2 codes, were also determined and then used to verify the developed stochastic model of the modernized signal. The differential code biases between P2 and C2, DCB P2-C2, were also estimated using the Bernese GPS software. A major residual error component, which affects the convergence of PPP solution, is the higherorder ionospheric delay. We rigorously modeled the second-order ionospheric delay, which represents the bulk of higher-order ionospheric delay, for our PPP model. First, we investigated the effect of second-order ionospheric delay on GPS satellite orbit and clock corrections. Second, we used the estimated satellite orbit and clock corrections to process the GPS data from several IGS stations after correcting the data for the effect of second-order ionospheric delay. The results demonstrated an improvement of up to 25% in the precision of the estimated coordinates with the second-order ionospheric delay, as well as reduction of the convergence time of the estimated parameters by about 15%, depending on the geographic location and ionospheric and geomagnetic conditions. Between-satellite single-difference PPP algorithms were developed to cancel out the receiver clock error, receiver initial phase bias, and receiver hardware delay. The decoupled clock corrections, provided by NRCan, were also applied to account for the satellite hardware delay and satellite initial phase bias. GPS data collected from several IGS stations were processed using the un-differenced model, un-differenced decoupled clock model, between-satellite singledifference (BSSD) model, and between-satellite single-difference using the decoupled clock (BSSD-DC) model. The results showed that the proposed BSSD model significantly improved the PPP convergence time by 50% and improved the solution precision by more than 60% over the traditional un-differenced PPP model.

An improved precise point positioning model using GPS and Galileo observations

Precise point positioning (PPP) allows for centimeter- to decimeter-level positioning accuracy using a single global navigation satellite system (GNSS) receiver. However, the use of PPP is presently limited due to the time required for the solution to converge or re-converge to the expected accuracy, which typically requires about 30 minutes. This relatively long convergence time is essentially caused by the existing un-modeled GNSS residual errors. Additionally, in urban areas, the number of visible satellites is usually limited when a single satellite constellation is used, which in turn slows down the PPP solution convergence. This, however, can be overcome by combining the observations of two constellations, namely the GPS and Galileo systems. Unfortunately, combining the GPS and Galileo constellations, although enhances the satellite geometry, introduces additional biases that must be considered in the observation mathematical models. These include the GPS-to-Galileo time offset, and Galileo satellite and receiver hardware delays. In addition, the stochastic characteristics of the new Galileo E1 and E5a signals must be determined to a high degree of precision. This can be done by analyzing various sets of GPS and Galileo measurements collected at two stations with short separation. Several PPP models are developed in this dissertation, which combine GPS and Galileo observations in the un-differenced and between-satellite single-difference (BSSD) modes. These include the traditional un-differenced model, the decoupled clock model, the semi-decoupled clock model, and the between-satellite single-difference model. It is shown that the traditional un-differenced GPS/Galileo PPP model, the GPS decoupled clock model, and semi-decoupled clock GPS/Galileo PPP model improve the convergence time by about 25% in comparison with the un-differenced GPS-only PPP model. In addition, the semi-decoupled GPS/Galileo PPP model improves the solution precision by about 25% compared to the traditional un-differenced GPS/Galileo PPP model. Moreover, the BSSD GPS/Galileo PPP model improves the solution convergence time by about 50%, in comparison with the un-differenced GPS PPP model, regardless of the type of BSSD combination used. As well, the BSSD model improves the solution precision by about 50% and 25% when the BSSD loose and tight combinations are used, respectively, in comparison with the un-differenced GPS-only model.

Performance Analysis of Several GPS/Galileo Precise Point Positioning Models

This paper examines the performance of several precise point positioning (PPP) models, which combine dual-frequency GPS/Galileo observations in the un-differenced and between-satellite single-difference (BSSD) modes. These include the traditional un-differenced model, the decoupled clock model, the semi-decoupled clock model, and the between-satellite single-difference model. We take advantage of the IGS-MGEX network products to correct for the satellite differential code biases and the orbital and satellite clock errors. Natural Resources Canada’s GPSPace PPP software is modified to handle the various GPS/Galileo PPP models. A total of six data sets of GPS and Galileo observations at six IGS stations are processed to examine the performance of the various PPP models. It is shown that the traditional un-differenced GPS/Galileo PPP model, the GPS decoupled clock model, and the semi-decoupled clock GPS/Galileo PPP model improve the convergence time by about 25% in comparison with the un-differenced GPS-only model. In addition, the semi-decoupled GPS/Galileo PPP model improves the solution precision by about 25% compared to the traditional un-differenced GPS/Galileo PPP model. Moreover, the BSSD GPS/Galileo PPP model improves the solution convergence time by about 50%, in comparison with the un-differenced GPS PPP model, regardless of the type of BSSD combination used. As well, the BSSD model improves the precision of the estimated parameters by about 50% and 25% when the loose and the tight combinations are used, respectively, in comparison with the un-differenced GPS-only model. Comparable results are obtained through the tight combination when either a GPS or a Galileo satellite is selected as a reference.

Performance Analysis of Several GPS/Galileo Precise Point Positioning Models

This paper examines the performance of several precise point positioning (PPP) models, which combine dual-frequency GPS/Galileo observations in the un-differenced and between-satellite single-difference (BSSD) modes. These include the traditional un-differenced model, the decoupled clock model, the semi-decoupled clock model, and the between-satellite single-difference model. We take advantage of the IGS-MGEX network products to correct for the satellite differential code biases and the orbital and satellite clock errors. Natural Resources Canada’s GPSPace PPP software is modified to handle the various GPS/Galileo PPP models. A total of six data sets of GPS and Galileo observations at six IGS stations are processed to examine the performance of the various PPP models. It is shown that the traditional un-differenced GPS/Galileo PPP model, the GPS decoupled clock model, and the semi-decoupled clock GPS/Galileo PPP model improve the convergence time by about 25% in comparison with the un-differenced GPS-only model. In addition, the semi-decoupled GPS/Galileo PPP model improves the solution precision by about 25% compared to the traditional un-differenced GPS/Galileo PPP model. Moreover, the BSSD GPS/Galileo PPP model improves the solution convergence time by about 50%, in comparison with the un-differenced GPS PPP model, regardless of the type of BSSD combination used. As well, the BSSD model improves the precision of the estimated parameters by about 50% and 25% when the loose and the tight combinations are used, respectively, in comparison with the un-differenced GPS-only model. Comparable results are obtained through the tight combination when either a GPS or a Galileo satellite is selected as a reference.