This paper focuses on the precise point positioning (PPP) ambiguity resolution (AR) using the observations acquired from four systems: GPS, BDS, GLONASS, and Galileo (GCRE). A GCRE four-system uncalibrated phase delay (UPD) estimation model and multi-GNSS undifferenced PPP AR method were developed in order to utilize the observations from all systems. For UPD estimation, the GCRE-combined PPP solutions of the globally distributed MGEX and IGS stations are performed to obtain four-system float ambiguities and then UPDs of GCRE satellites can be precisely estimated from these ambiguities. The quality of UPD products in terms of temporal stability and residual distributions is investigated for GPS, BDS, GLONASS, and Galileo satellites, respectively. The BDS satellite-induced code biases were corrected for GEO, IGSO, and MEO satellites before the UPD estimation. The UPD results of global and regional networks were also evaluated for Galileo and BDS, respectively. As a result of the frequency-division multiple-access strategy of GLONASS, the UPD estimation was performed using a network of homogeneous receivers including three commonly used GNSS receivers (TRIMBLE NETR9, JAVAD TRE_G3TH DELTA, and LEICA). Data recorded from 140 MGEX and IGS stations for a 30-day period in January in 2017 were used to validate the proposed GCRE UPD estimation and multi-GNSS dual-frequency PPP AR. Our results show that GCRE four-system PPP AR enables the fastest time to first fix (TTFF) solutions and the highest accuracy for all three coordinate components compared to the single and dual system. An average TTFF of 9.21 min with $$7{^{\circ }}$$
 cutoff elevation angle can be achieved for GCRE PPP AR, which is much shorter than that of GPS (18.07 min), GR (12.10 min), GE (15.36 min) and GC (13.21 min). With observations length of 10 min, the positioning accuracy of the GCRE fixed solution is 1.84, 1.11, and 1.53 cm, while the GPS-only result is 2.25, 1.29, and 9.73 cm for the east, north, and vertical components, respectively. When the cutoff elevation angle is increased to $$30{^{\circ }}$$
 , the GPS-only PPP AR results are very unreliable, while 13.44 min of TTFF is still achievable for GCRE four-system solutions.

Multi-GNSS phase delay estimation and PPP ambiguity resolution: GPS, BDS, GLONASS, Galileo

Unmanned aerial vehicles (UAVs) play a primary role in a plethora of technical and scientific fields owing to their wide range of applications. In particular, the provision of emergency services during the occurrence of a crisis event is a vital application domain where such aerial robots can contribute, sending out valuable assistance to both distressed humans and rescue teams. Bearing in mind that time constraints constitute a crucial parameter in search and rescue (SAR) missions, the punctual and precise detection of humans in peril is of paramount importance. The paper in hand deals with real-time human detection onboard a fully autonomous rescue UAV. Using deep learning techniques, the implemented embedded system was capable of detecting open water swimmers. This allowed the UAV to provide assistance accurately in a fully unsupervised manner, thus enhancing first responder operational capabilities. The novelty of the proposed system is the combination of global navigation satellite system (GNSS) techniques and computer vision algorithms for both precise human detection and rescue apparatus release. Details about hardware configuration as well as the system’s performance evaluation are fully discussed.

Unsupervised Human Detection with an Embedded Vision System on a Fully Autonomous UAV for Search and Rescue Operations

This paper focuses on the contribution of the global positioning system (GPS) and BeiDou navigation satellite system (BDS) observations to precise point positioning (PPP) ambiguity resolution (AR). A GPS + BDS fractional cycle bias (FCB) estimation method and a PPP AR model were developed using integrated GPS and BDS observations. For FCB estimation, the GPS + BDS combined PPP float solutions of the globally distributed IGS MGEX were first performed. When integrating GPS observations, the BDS ambiguities can be precisely estimated with less than four tracked BDS satellites. The FCBs of both GPS and BDS satellites can then be estimated from these precise ambiguities. For the GPS + BDS combined AR, one GPS and one BDS IGSO or MEO satellite were first chosen as the reference satellite for GPS and BDS, respectively, to form inner-system single-differenced ambiguities. The single-differenced GPS and BDS ambiguities were then fused by partial ambiguity resolution to increase the possibility of fixing a subset of decorrelated ambiguities with high confidence. To verify the correctness of the FCB estimation and the effectiveness of the GPS + BDS PPP AR, data recorded from about 75 IGS MGEX stations during the period of DOY 123-151 (May 3 to May 31) in 2015 were used for validation. Data were processed with three strategies: BDS-only AR, GPS-only AR and GPS + BDS AR. Numerous experimental results show that the time to first fix (TTFF) is longer than 6 h for the BDS AR in general and that the fixing rate is usually less than 35 % for both static and kinematic PPP. An average TTFF of 21.7 min and 33.6 min together with a fixing rate of 98.6 and 97.0 % in static and kinematic PPP, respectively, can be achieved for GPS-only ambiguity fixing. For the combined GPS + BDS AR, the average TTFF can be shortened to 16.9 min and 24.6 min and the fixing rate can be increased to 99.5 and 99.0 % in static and kinematic PPP, respectively. Results also show that GPS + BDS PPP AR outperforms single-system PPP AR in terms of convergence time and position accuracy.

Ambiguity resolved precise point positioning with GPS and BeiDou

The focus of this study is on proper modeling of the dynamics for inter-system biases (ISBs) in multi-constellation Global Navigation Satellite System (GNSS) precise point positioning (PPP) processing. First, the theoretical derivation demonstrates that the ISBs originate from not only the receiver-dependent hardware delay differences among different GNSSs but also the receiver-independent time differences caused by the different clock datum constraints among different GNSS satellite clock products. Afterward, a comprehensive evaluation of the influence of ISB stochastic modeling on undifferenced and uncombined PPP performance is conducted, i.e., random constant, random walk process, and white noise process are considered. We use data based on a 1-month period (September 2017) Multi-GNSS Experiment (MGEX) precise orbit and clock products from four analysis centers (CODE, GFZ, CNES, and WHU) and 160 MGEX tracking stations. The results demonstrate that generally, the positioning performance of PPP in terms of convergence time and positioning accuracy with the final products from CODE, CNES, and WHU is comparable among the three ISB handling schemes. However, estimating ISBs as random walk process or white noise process outperforms that as the random constant when using the GFZ products. These results indicate that the traditional estimation of ISBs as the random constant may not always be reasonable in multi-GNSS PPP processing. To achieve more reliable positioning results, it is highly recommended to consider the ISBs as random walk process or white noise process in multi-GNSS PPP processing.

Influence of stochastic modeling for inter-system biases on multi-GNSS undifferenced and uncombined precise point positioning

In the past few decades, global navigation satellite system (GNSS) technology has been widely used in structural health monitoring (SHM), and the monitoring mode has evolved from long-term deformation monitoring to dynamic monitoring. This paper gives an overview of GNSS-based dynamic monitoring technologies for SHM. The review is classified into three parts, which include GNSS-based dynamic monitoring technologies for SHM, the improvement of GNSS-based dynamic monitoring technologies for SHM, as well as denoising and detrending algorithms. The significance and progress of Real-Time Kinematic (RTK), Precise Point Position (PPP), and direct displacement measurement techniques, as well as single-frequency technology for dynamic monitoring, are summarized, and the comparison of these technologies is given. The improvement of GNSS-based dynamic monitoring technologies for SHM is given from the perspective of multi-GNSS, a high-rate GNSS receiver, and the integration between the GNSS and accelerometer. In addition, the denoising and detrending algorithms for GNSS-based observations for SHM and corresponding applications are summarized. Challenges of low-cost and widely covered GNSS-based technologies for SHM are discussed, and problems are posed for future research.

https://www.mdpi.com/2072-4292/11/9/1001/pdf

A Review of Global Navigation Satellite System (GNSS)-Based Dynamic Monitoring Technologies for Structural Health Monitoring

The invention discloses a method for quickly calculating the fuzzy degree between RTK reference stations in a Beidou-compatible GPS/GLONASS network. In a network RTK model, the calculation accuracy of the fuzzy degree between RTK reference stations has a direct bearing on the precision of space error modeling of the troposphere, the ionosphere, and the like. In the multi-constellation fusion network RTK, the number of satellites increases significantly, which provides favorable conditions for quickly calculating the fuzzy degree. According to the invention, first, a high-altitude angle satellite is fixed according to a network RTK fuzzy degree calculation model with atmospheric delay priori information, and then, calculation is performed based on the observed value constraint atmospheric delay of a satellite of which the fuzzy degree is fixed and an observation equation corresponding to a low-altitude angle satellite or a newly rising satellite or an unlocked recaptured satellite so as to quickly fix the fuzzy degree of a satellite of which the fuzzy degree is not fixed. By adopting the method, the time for calculating the fuzzy degree between network RTK reference stations is obviously shortened, and the precision and reliability of network RTK positioning are improved.

Method for quickly calculating fuzzy degree between RTK reference stations in Beidou-compatible GPS/GLONASS network

Precise Point Positioning (PPP) has been demonstrated to be a powerful tool in geodetic applications, such as deformation monitoring. Troposphere delay is an important error source which directly affects positioning accuracy in height direction. At the end of 2012, an improved model named Global Pressure and Temperature 2 (GPT2) was proposed. Compared with early empirical models, this new model mainly eliminates the weakness of limited spatial and temporal variability. In this study, we assess the precision of GPT2 model and apply it in PPP analysis. The analysis data of VMF1, which is produced using European Centre for Medium-Range Weather Forecasts (ECMWF), provides the nearly true value of zenith delays and mapping function for International GNSS Service (IGS) stations. Therefore a globally distributed set of 11 IGS stations is chosen to validate GPT2 model. In the case of using GPT2 as a priori model while the residual zenith delay still estimated with other unknown parameters, it would improve the zenith troposphere delay adjustments to nearly zero-mean. Therefore GPT2 is helpful to improve the efficiency in PPP data processing. However, GPT2 model is only resting upon a global 5° grid and sufficient global troposphere models are not yet available. Due to the complexity of wet zenith delay, PPP height solutions would be unsatisfactory when residual troposphere delay is not parameterized. We conclude that GPT2 is capable of predicting troposphere delay worldwide with an acceptable uncertainty. And GPT2-based PPP solution performs well only in the case of regarding residual model error as an additional unknown parameter.

Assessment of GPT2 Empirical Troposphere Model and Application Analysis in Precise Point Positioning

In the process of composing a double-differenced positioning model, it is difficult to separate different frequency signals between code division multiple access (CDMA) systems, the single-difference ambiguity of the pivot satellite and phase differential inter-system biases (PDISBs). Hence it is difficult to calibrate in advance the bias between systems in order to build an inter-system model which only needs one pivot satellite. Based on analysis of the stability of PDISB parameters for non-overlapping frequency CDMA systems, this study adopts a particle filter to estimate the fractional part of the PDISBs (F-PDISBs) between the systems and proposes a particle filter-based inter-system positioning model. Results show that the F-PDISBs and code DISBs for the baselines with the same receiver types and some with different receiver types are rather stable over time and for these baselines it is feasible to use a particle filter to estimate the F-PDISB parameters in the initial stage. Having attained the F-PDISBs, the inter-system model can be constructed to improve positioning accuracy in complex operational environments.

https://www.cambridge.org/core/services/aop-cambridge-core/content/view/DD142D4DDDE08FDC58923EA824FB9357/S0373463320000077a.pdf/div-class-title-particle-filter-based-inter-system-positioning-model-for-non-overlapping-frequency-code-division-multiple-access-systems-div.pdf

Particle Filter-Based Inter-System Positioning Model for Non-Overlapping Frequency Code Division Multiple Access Systems

When calibrating inter-system biases (ISB), especially the fractional part of inter-system phase biases (F-ISPB), a multi-GNSS inter-system model can effectively improve positioning performance under a complex environment. Usually, the F-ISPB is estimated after fixing the intra-system ambiguities. However, this approach seems inapplicable when it is difficult to obtain intra-system ambiguities under a complex environment. A multi-dimensional particle filter (PF)-based F-ISPB estimate method has been proposed to overcome the problem. Nevertheless, the multi-dimensional PF involves a great quantity of computations. In this contribution, four state optimal estimate-based F-ISPB handling schemes are proposed: step-by-step PF, step-by-step particle swarm optimization (PSO), multi-dimensional PF, and multi-dimensional PSO-based F-ISPB estimate methods. Two baselines were selected to investigate the F-ISPB estimate performance in both open and complex environments. The results show that due to the potential of the wrong F-ISPB to bring about the maximum ratio for a long time during the initial stage, the step-by-step PF method can achieve better performance than step-by-step PSO. Besides, the two-dimensional results show that all of the F-ISPB still cannot be extracted under complex environments by multi-dimensional PSO. Furthermore, compared with step-by-step PF, the multi-dimensional PF method costs too much to obtain the right value. For example, in the two-dimensional case, the step-by-step PF searches 200 times for each epoch, while the two-dimensional PF requires 40 000 times for each epoch, so it is difficult for receivers to provide hardware support for this method. In addition, the step-by-step PF can obtain the right F-ISPB with about 100 epochs no matter what scenario. Thus, under challenging observation scenarios, a step-by-step PF method is recommended to extract the F-ISPB.

Multi-GNSS inter-system model for complex environments based on optimal state estimation

Gao, C.; Gao, W.; Pan, S.; Chen, W.; Shi, X., and Wang, D., 2015. High-precision oceanic real-time positioning application based on regional continuous operation reference stations. With the rapid development of land-based Global Navigation Satellite System (GNSS) positioning technology, the high-precision oceanic GNSS positioning demands are more extensive. A real-time precise point positioning (PPP) method based on regional Continuous Operation Reference Stations (CORS) for ocean application is proposed in the paper. Starting fromthe requirement of real-time satellite orbit and clock products for the real-time PPP, the accuracy of IGU (International GNSS service Ultra-rapid) orbit product and its correlation with prediction time were analyzed. Also, a new easy and effective real-time estimation method of satellite clock based on regional CORS network was proposed, and the estimated clock was compared with the IGS final clock products. Then based on predicted IGU orbit and estimated real-time cl...

https://bioone.org/journals/journal-of-coastal-research/volume-73/issue-sp1/SI73-057.1/High-Precision-Oceanic-Real-Time-Positioning-Application-Based-on-Regional/10.2112/SI73-057.1.pdf

Chengfa Gao

Papers

Method for quickly calculating fuzzy degree between RTK reference stations in Beidou-compatible GPS/GLONASS network

Assessment of GPT2 Empirical Troposphere Model and Application Analysis in Precise Point Positioning

Particle Filter-Based Inter-System Positioning Model for Non-Overlapping Frequency Code Division Multiple Access Systems

Multi-GNSS inter-system model for complex environments based on optimal state estimation

High-Precision Oceanic Real-Time Positioning Application Based on Regional Continuous Operation Reference Stations