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

A new single-epoch ambiguity resolution for long baseline RTK, on the basis of the ionosphere-weighted model, is proposed in this paper, which is applicable to several hundred km baselines by using linear combination of the measurements, including the double-differential wide-lane combination and ionosphere-free combination carrier-phase observation equations and UofC model. By the correct ambiguity and double-differential atmosphere delay, the real-time atmosphere model was established to provide predicted value for a new risen satellite. The establishment of real-time atmosphere predicted model, estimating the relative tropospheric zenith delay and the regional ionospheric parameters, is proved to reach an acceptable accuracy. Test data from the multiple reference station GPS networks in Chongqing, consisting of 36 baselines from 40 to 350 km, were used to evaluate the performance of the proposed approach. It is showed that the proposed algorithm can reduce the effect of the ionosphere and troposphere on the network AR and the true integer ambiguity could be achieved in a short time after the establishment of the predicted atmosphere model.

Single-Epoch Integer Ambiguity Resolution for Long-Baseline RTK with Ionosphere and Troposphere Estimation

Aiming to the disadvantages of traditional triangle network solution at present, this paper proposed a regional modeling method of atmosphere delay in network RTK based on utilizing redundant observation information of multiple reference station in regional GNSS network. The algorithm of ionosphere and troposphere delay corrections in network RTK is studied respectively. At last, the precisions of traditional triangulation linear interpolation model and regional model proposed by this paper are analyzed using measured data from American CORS. The results show that the precision of ionosphere delay correction interpolation model based on multiple reference station adopted by this paper is similar to that of traditional triangulation linear interpolation model. The precision of troposphere delay correction interpolation model concerning with elevation based on multiple reference station adopted by this paper is superior to the traditional triangulation linear interpolation model and is more stable. Especially for the satellites with low-elevation angles, its precision of interpolation model are much higher than those of traditional triangulation linear interpolation model.

Regional Modeling of Atmosphere Delay in Network RTK Based on Multiple Reference Station and Precision Analysis

Routine Network Real-time Kinematic (NRTK) techniques always use triangle as the interpolation unit to solve the users’ ambiguity and atmospheric effects. Because of the limitation of the calculating unit, the correction information based on virtual reference station (VRS) technique cannot fully use the information from all the reference stations, so that the users’ RTK initialization speed and positioning accuracy would be seriously affected. In order to avoid the loss of observation information, two interpolation methods, MLIM for ionosphere and RELIM for troposphere, were proposed in the paper. Relying on the existing Delaunay Triangulated Network (DTN) structure, the optimal and suboptimal units were selected to increase the interpolation baselines number. Using the data from the Earth Scope Plate Boundary, our methods obtained more accurate interpolation accuracy by 3 times and 6 to 30 times higher for ionosphere and troposphere, respectively, compared with the traditional models. Besides, these two models can get more stable performance regardless the change of satellites elevation and the altitude differences among reference stations. In addition, we developed an alternative integrity monitoring method for rover station, with the interpolating monitoring precision of centimeter level. The proposed method is feasible to evaluate the actual integrity index and monitor the actual accuracy in the users’ position.

A Multi-Redundancies Network RTK Atmospheric Errors Interpolation Method Based on Delaunay Triangulated Network

One critical issue in network real-time kinematic (NRTK) is the interpolation of atmospheric delay for user stations. Some classic interpolation algorithms, such as linear interpolation method (LIM), ignore the strong correlation between tropospheric delay and height factors, and the interpolation accuracy is poor in areas with large height difference. To solve this problem, a troposphere modelling method based on error compensation, namely ECDIM (Error Compensation-Based DIM), is proposed, and this method can be applied to both conventional single Delaunay triangulated network (DTN) and multi-station scenarios. The results of California Real Time Network (CRTN) with large height difference show that compared with LIM, the overall modelling accuracy with ECDIM has been improved by 50.1% to 67.3%, and especially for low elevation satellites (e.g., 10–20 degree), the accuracy is increased from tens of centimetres to a few centimetres. At user end, the positioning error in up direction with LIM has an obvious systematic deviation, and the fix rate of epoch is relatively low. This situation has been improved significantly after using ECDIM. The results of Tianjin Continuously Operating Reference System (TJCORS) show that in areas with small height difference, both methods have achieved high precision interpolation accuracy, and the positioning accuracy with ECDIM in up direction is improved by 21.2% compared with LIM.

https://www.cambridge.org/core/services/aop-cambridge-core/content/view/C03757C141BA529E16E33B97FABC3556/S037346332000034Xa.pdf/div-class-title-a-multi-station-troposphere-modelling-method-based-on-error-compensation-considering-the-influence-of-height-factor-div.pdf

Chengfa Gao

Papers

Single-Epoch Integer Ambiguity Resolution for Long-Baseline RTK with Ionosphere and Troposphere Estimation

Regional Modeling of Atmosphere Delay in Network RTK Based on Multiple Reference Station and Precision Analysis

A Multi-Redundancies Network RTK Atmospheric Errors Interpolation Method Based on Delaunay Triangulated Network

A Multi-Station Troposphere Modelling Method Based on Error Compensation Considering the Influence of Height Factor

NLOS signal detection and correction for smartphone using convolutional neural network and variational mode decomposition in urban environment