Evaluation of Post-Processing Kinematic (PPK) Accuracy in Urban Area in Turgutlu, Manisa, Türkiye

The integration of survey-grade Global Navigation Satellite Systems (GNSS) with photogrammetric measurement studies conducted with an aircraft has the potential to meet the need for the use of ground control points (GCP). In photogrammetric studies, there are different GNSS techniques to provide the precise positioning of the aircraft. The precise point positioning (PPP) technique has become an alternative method to post-processing kinematic (PPK) methods in the evaluation of GNSS data gathered with an aircraft at high speed. In this study, GNSS data gathered from two different flights conducted for photogrammetric surveys in Izmir, Turkey were processed with different software (RTKLIB, gLAB, CSRS-PPP, and GRAFNAV) through PPP technique with the ambiguity resolution (PPP-AR) method to analyse the performance of this method versus PPK. Three-dimensional (3D) positioning analyses of coordinate differences obtained from mapping flights demonstrated a variation pattern mainly between 0 and 6 cm for both kinematic PPP and PPK results.

Abstract. Utilizing ground control points (GCPs) to georeference photogrammetry-based point cloud data is a common practice in unmanned aerial system (UAS) mapping. Direct georeferencing or integrated sensor orientation (ISO) can be used to obtain georeferenced point clouds from UAS without relying heavily on GCPs. However, the accuracy of the point cloud may be impacted by the accuracy of the trajectory solution obtained by GNSS. To improve point cloud accuracy, post-processing kinematic (PPK) solutions can be applied to the UAS trajectory, which may provide higher accuracy than low-accuracy trajectory solutions and minimize the reliance on GCPs. This study compares the accuracy and precision of two different point clouds generated using different methods. One point cloud was generated using traditional photogrammetric methods with low accuracy Global Navigation Satellite System (GNSS) observations from the UAS and GCPs that have an average accuracy of one to two centimeters, while the other was generated using PPK trajectory solution for the UAS’s trajectory with two software: open-source Emlid Studio and the widely used Inertial Explorer. The use of PPK techniques in UAS mapping may have several potential benefits over traditional methods. By correcting the errors in the UAS's trajectory, a user may only need to depend on fewer ground control points, which can reduce the time and cost associated with fieldwork. This is particularly useful in areas that are difficult to access or have limited ground control point options, such as in urban or forested areas. To evaluate performance, a GNSS receiver is used to obtain measurements on checkpoints, which are used to assess the accuracies of the point clouds. In our experiments, the accuracy of the point clouds generated using PPK trajectory solution with high accuracy GCPs was found to be higher than those generated with low accuracy GNSS observations while aided with high accuracy ground control points. While the use of PPK with GCPs is generally expected to provide more accurate and reliable data than low-accuracy GNSS observations even after adjusting with GCPs, the number and distribution of GCPs can still significantly impact overall accuracy. Therefore, careful consideration of the number of GCPs and their placement is essential to achieve the desired level of efficiency and effectiveness in UAS mapping.

As the necessity of location information closely related to everyday life has increased, the use of global navigation satellite systems (GNSS) has gradually increased in populated urban areas. Contrary to the high necessity and expectation of GNSS in urban areas, GNSS performance is easily degraded by multipath errors due to high-rise buildings and is very difficult to guarantee. Errors in the signals reflected by the buildings, i.e., multipath and non-line-of-sight (NLOS) errors, are the major cause of the poor accuracy in urban areas. Unlike other GNSS major error sources, the reflected signal error, which is a user-dependent error, is difficult to differentiate or model. This paper suggests training a multipath prediction model based on support vector regression to obtain a function of the elevation and azimuth angle of each satellite. To extract an unbiased multipath from the GNSS measurements, the clock error of high-elevation QZSS was estimated, and the clock offset with other constellations was also calculated. A nonlinear multipath map was generated, as a result of training with the extracted multipaths, by a Support Vector Machine, which appropriately reflected the geometry of the building near the user. The model was effective at improving the urban area positioning accuracy by 58.4% horizontally and 77.7% vertically, allowing us to achieve a 20 m accuracy level in a deep urban area, Teheran-ro, Seoul, Korea.

Uncrewed aircraft systems (UASs) and structure-from-motion/multi-view stereo (SfM/MVS) photogrammetry are efficient methods for mapping terrain at local geographic scales. Traditionally, indirect georeferencing using ground control points (GCPs) is used to georeference the UAS image locations before further processing in SfM software. However, this is a tedious practice and unsuitable for surveying remote or inaccessible areas. Direct georeferencing is a plausible alternative that requires no GCPs. It relies on global navigation satellite system (GNSS) technology to georeference the UAS image locations. This research combined field experiments and simulation to investigate GNSS-based post-processed kinematic (PPK) as a means to eliminate or reduce reliance on GCPs for shoreline mapping and charting. The study also conducted a brief comparison of real-time network (RTN) and precise point positioning (PPP) performances for the same purpose. Ancillary experiments evaluated the effects of PPK base station distance and GNSS sample rate on the accuracy of derived 3D point clouds and digital elevation models (DEMs). Vertical root mean square errors (RMSEz), scaled to the 95% confidence interval using an assumption of normally-distributed errors, were desired to be within 0.5 m to satisfy National Oceanic and Atmospheric Administration (NOAA) requirements for nautical charting. Simulations used a Monte Carlo approach and empirical tests to examine the influence of GNSS performance on the quality of derived 3D point clouds. RTN and PPK results consistently yielded RMSEz values within 10 cm, thus satisfying NOAA requirements for nautical charting. PPP did not meet the accuracy requirements but showed promising results that prompt further investigation. PPK experiments using higher GNSS sample rates did not always provide the best accuracies. GNSS performance and model accuracies were enhanced when using base stations located within 30 km of the survey site. Results without using GCPs observed a direct relationship between point cloud accuracy and GNSS performance, with R2 values reaching up to 0.97.

There is a growing need for fusion and convergence technology between UAV (Unmanned Aerial Vehicle)s, which is attracting attention as a new growth engine for spatial information in the future. This need bolsters research efforts to make use of UAV and location positioning technologies based on their fusion and convergence in various fields. Therefore, this study set out to assess georeferencing method techniques based on RTK (Real Time Kinematic), PPK (Post Processed Kinematic), and GCP (Ground Control Point), which are types of UAV photogrammetry based on GNSS (Global Navigation Satellite System) positioning technology on the basis of fusion and convergence between UAVs, whose utilization began in survey, architecture, forestry, and disaster prevention, and location positioning technologies in location accuracy, work time, and accessibility of final results (mapping). The study also aimed to propose a technique of UAV photogrammetry based on GNSS positioning technologies capable of offering options according to areas of application, including UAV-based aerial photogrammetry, disaster prevention in agriculture, inspection of construction and structures, and forest survey. These techniques were applied to a research lot of approximately 0.6 Km2, and their outcomes were analyzed. The analysis results show that RTK and PPK had all of their operations automated and were thus able to reduce work time and GCP volume in the field. They could, however, cause problems with checking survey outcomes or testing their accuracy, except for the georeferencing approach based on GCP. These outcomes were arranged according to the survey methods: RTK will be applicable to areas where its real-time application is possible; PPK will be applicable to areas characterized by faster work times, small volume of indoor work, and medium-level accuracy in the field; and the GCP-based georeferencing technique will be applicable to areas that require a lot of work time and high accuracy. The findings of the study provide information about appropriate techniques and their advantages and disadvantages for each area of application. The study also offered a set of criteria to apply UAV photogrammetry techniques to the environment, ecology, and disaster prevention as well as spatial imagery information, thus considerably improving the efficiency of related jobs.

In urban environments, Global Navigation Satellite Systems (GNSS) signals are frequently attenuated, blocked or reflected, which degrades the positioning accuracy of GNSS receivers significantly. To improve the performance of GNSS receiver for vehicle urban navigation, a GNSS/INS deeply-coupled software defined receiver (GIDCSR) with a low cost micro-electro-mechanical system (MEMS) inertial measurement unit (IMU) ICM-20602 is presented, in which both GPS and BDS constellations are supported. Two key technologies, that is, adaptive open-close tracking loops and INS aided pseudo-range weight control algorithm, are applied in the GIDCSR to enhance the signal tracking continuity and positioning accuracy in urban areas. To assess the performance of the proposed deep couple solution, vehicle field tests were carried out in GNSS-challenged urban environments. With the adaptive open-close tracking loops, the deep couple output carrier phase in the open sky, and improved pseudo-range accuracy before and after GNSS signal blocked. Applying the INS aided pseudo-range weight control, the pseudo-range gross errors of the deep couple decreased caused by multipath. A popular GNSS/INS tightly-coupled vehicle navigation kit from u-blox company, M8U, was tested side by side as benchmark. The test results indicate that in the GNSS-challenged urban areas, the pseudo-range quality of GIDCSR is at least 25% better than that of M8U, and GIDCSR’s horizontal positioning results are at least 69% more accurate than M8U’s.

Abstract. GNSS stands for Global Navigation Satellite System and is the standard generic term for satellite navigation systems that provide autonomous geo-spatial positioning with global coverage. The advantage of having access to multiple satellites is accuracy, redundancy, and availability at all the times. Though satellite systems do not often fail, if one fails GNSS receivers can pick up signals from other systems. If the line of sight is obstructed, having access to multiple satellites is also a benefit. GPS (Global Positioning System, USA), GLONASS (Global Navigation Satellite System, Russia), BeiDou (Compass, China), and some regional systems are positioning systems that are usually used. In recent years with the development of the UAVs and GNSS receivers, it is possible to manage an accurate PPK (Post Processing Kinematic) networks with a GNSS receiver mounted on a UAV to achieve the position of images principal points WGS1984 and to reduce the need for GCPs. But the most important challenge in a PPK task is, which a combination of different GNSS constellations would result in the most accurate computed position in checkpoints. For this purpose, this study focused on a PPK equipped UAV to map an open pit (Golgohar mine near Sirjan city). For the purpose, different combination of GPS, GLONASS and BeiDou used for position computed. Results are plotted and compared and found out having access to multiple constellations while doing a PPK task would bring higher accuracies in building photogrammetric models although it may cause some random error due to the higher values of noise while the number of the satellites increases.

There is a lack of effective testing methods to evaluate high-precision global navigation satellite system (GNSS) kinematic positioning solutions, such as GNSS real-time kinematic (RTK) or post-processed kinematic (PPK), with centimeter-level accuracy. Current methods either process static GNSS data in kinematic mode to perform a pseudo-kinematic test or use a precise motion table to make a real-kinematic test but within a very limited travel distance. This study proposes a trajectory similarity method by moving a track trolley platform along a railway track, which can match the GNSS positioning trajectory and the pre-surveyed reference track. The GNSS trajectory offsets from the reference track along the cross-track and vertical directions are regarded as GNSS kinematic positioning errors. Lever-arm compensation is applied to achieve millimeter-level accuracy for this evaluation method. A case study was conducted to evaluate the positioning performance of the Global Positioning System/BeiDou Navigation Satellite System (GPS/BDS) PPK using the proposed method. The results indicate that the proposed method can provide a reference trajectory on the order of a few millimeters, which is sufficiently accurate even for PPK positioning performance evaluation and error source tracing in wide regions. In this case, cycle slips as small as 10 cm in the carrier phase measurements can be detected and studied based on the proposed method.

Non-line-of-sight (NLOS) global navigation satellite system (GNSS) signals are a major factor that limits the GNSS positioning accuracy in urban areas. An advanced GNSS signal processing technique, the vector tracking loop (VTL), has been applied to NLOS detection and correction, and its feasibility and superior performance have been reported in recent studies. In a VTL-based GNSS receiver, the navigation solutions (i.e., position, velocity and time (PVT)) are used to predict the signal tracking loop parameters. The difference between the predicted signal and the received signal within the code discriminator output can be used to detect NLOS reception. We generate the probability of NLOS detection by modeling the code discriminator outputs using Gaussian fitting. If this probability is larger than a predefined threshold, NLOS reception is deemed to occur. Then, the NLOS-induced pseudorange measurement bias is estimated as a state variable in the state vector, i.e., an augmented state vector is created for the extended Kalman filter. Two GPS L1 C/A signal datasets from a static test and a dynamic test are investigated using the proposed algorithm. The experimental results indicate that when NLOS reception is present, the proposed approach outperforms the other two methods, i.e., the standard VTL method without considering NLOS reception and the VTL-based NLOS detection and correction method with multicorrelators, in terms of the positioning performance. In addition, the proposed approach has a lower computational load than the VTL method with multicorrelators.

Abstract. Developing an accurate water level monitoring system is one of the measures to mitigate the effects of water-related hazards such as river flooding. While current monitoring systems in the country are efficient in terms of accurate and immediate data delivery, these systems can be costly. This study assesses the Global Navigation Satellite System (GNSS) performance of low-cost receiver systems for water level monitoring using real-time kinematic (RTK) solution. A total of 10 days’ valid observation were analyzed to compare the two base-rover receiver setups: 1) low-cost base to low-cost rover (LC-LC) and 2) survey-grade base to low-cost rover (SG-LC) grounded on accuracy, integrity, continuity, availability, and cost. Accuracy results show LC-LC=5.81 cm and SG-LC=5.37 cm mean difference of RTK from in-situ readings. In terms of RTK and post-processing kinematic (PPK) difference for integrity criterion, the RTK SG-LC setup has a lower range of RMS of 0.86 to 1.94 cm versus LC-LC setup of 1.19 to 2.28 cm. For the continuity criterion, the average fixed solutions percentage for the LC-LC setup: RTK=91.43%, PPK=92.92%, whereas for the SG-LC: RTK=95.51%, PPK=98.39%. On availability, the number of valid satellites (NSat) and position dilution of precision (PDOP) of RTK and PPK solutions for each setup are LC-LC: RTK=11, PPK=23, PDOP=1.0 and SG-LC: RTK=11, PPK=24, PDOP=1.9. Lastly, in terms of costing, LC-LC costs Php 58,340 while SG-LC costs Php 1,279,645. Overall, the parity of LC-LC with SG-LC in terms of the five criteria suggests viability of using LC-LC for accurate real-time water level monitoring.

This paper describes our solution for the Google smartphone decimeter challenge (GSDC), which was held from May to August 2022. The GSDC is a competition for improving positioning accuracy of smartphones. The global navigation satellite system (GNSS) data from smartphones have lower signal levels and higher noise in GNSS observations compared to commercial GNSS receivers. Therefore, it is difficult to directly apply the existing GNSS high-precision positioning methods like precise point positioning (PPP) and real-time kinematic (RTK). The smartphones used to collect the raw GNSS data have multi-constellation, dual-frequency GNSS receivers, and Inertial Measurement Unit (IMU) sensors. Multi-sensor fusion technology has become very prominent for seamless navigation systems due to its complementary capabilities to GNSS positioning. In this work, we developed a machine learning (ML) based adaptive positioning approach to estimate the positions of the smartphone by utilizing post-processed kinematic (PPK) precise positioning techniques to process the GNSS datasets. The ML model is used to predict the driving paths (highways, tree-lined streets, or downtown areas). Depending on the predicted driving path, PPK technique uses the carrier phase to compute the user position using differential corrections from known GNSS base stations. We then use of the Rauch–Tung–Striebel (RTS) smoother, which consists of a forward pass Kalman Filter (KF) and a backward recursion smoother to achieve a loosely coupled integration of GNSS and IMU measurements for positioning estimation of the smartphone. We refer to this method as LC-GNSS/IMU/ML using ML based adaptive positioning (MAP) real-time kinematic (RTK) post-processing algorithm (MAP RTK). This method is validated using reference data from GNSS survey-grade receivers provided with the training datasets. The final validation of this proposed method is done on Kaggle.com, the host of the GSDC competition. Using the proposed method, we estimated the location of the smartphone and tackled the competition. The final public score was 2.61 m, while the final private score was 2.29 m.

The MarineStar Global Navigation Satellite System (GNSS) augmentation services are widely used in bathymetric surveys to obtain the sensor position. The postprocessed kinematic (PPK) GNSS method can be used to obtain more accurate positions. Therefore, the present work aims to verify the vertical final accuracies and uncertainties of exclusive order multibeam hydrographic surveys using the Global Navigation Satellite System (GNSS) as a horizontal positioning tool through methods such MarineStar services and PPK relative positioning, with comparisons between the two methods based on the results of the hydrographic base created. We produced two surfaces with each method, representing the minimum and maximum depths of the bathymetry point cloud. A statistical analysis of the differences between the depth surfaces obtained with the two methods was carried out. When comparing the two surfaces using the PPK method, the difference was less than 10 cm at 95.1% of the points. When comparing the two surfaces to those of the MarineStar, the difference between the maximum and minimum depths was less than 10 cm at 93.1% of the points. The results suggest that using the same survey data, the products obtained with the PPK method would comply with the hydrographic standards, while the MarineStar products would not.

Identification of dynamic displacements and modal frequencies of a medium-span suspension bridge using multimode GNSS processing

Sea surface height measurements using a low-cost GNSS buoy with multiple GNSS receivers

This paper investigated the achievable accuracy from a low-cost RTK (Real Time Kinematic)/PPK (Post Processing Kinematic) GNSS (Global Navigation Satellite Systems) system installed on board a UAV (Unmanned Aerial Vehicle), employing three different types of GNSS Bases (Alloy, RS2 and RING) working in PPK mode. To evaluate the quality of the results, a set of seven GCPs (Ground Control Points) measured by means of the NRTK (Network Real Time Kinematic) technique was used. The outcomes show a RMSE (Root Mean Square Error) of 0.0189 m for an ALLOY Base, 0.0194 m for an RS2 Base and 0.0511 m for RING Base, respectively, on the vertical value of DEMs (Digital Elevation Models) obtained by a photogrammetric process. This indicates that, when changing the Base for the PPK, the solutions are different, but they can still be considered adequate for precision positioning with UAVs, especially when GCPs could be used with some difficulty. Therefore, the integration of a RTK/PPK GNSS module on a UAV allows the reconstruction of a highly detailed and precise DEM without using GCPs and provides the possibility to carry out surveys in inaccessible areas.