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Academic literature on the topic 'GNSS raw measurement'

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Published: 14 March 2023

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Journal articles on the topic "GNSS raw measurement"

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Bosser, Pierre, Joël Van Baelen, and Olivier Bousquet. "Routine Measurement of Water Vapour Using GNSS in the Framework of the Map-Io Project." Atmosphere 13, no. 6 (June 2, 2022): 903. http://dx.doi.org/10.3390/atmos13060903.

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The "Marion Dufresne Atmospheric Program - Indian Ocean" (MAP-IO) project is a research program that aims to collect long-term atmospheric observations in the under-instrumented Indian and Austral Oceans. As part of this project, a Global Navigation Satellite System (GNSS) antenna was installed on the research vessel (R/V) Marion Dufresne in October 2020. GNSS raw data is intended to be used to retrieve Integrated Water Vapour (IWV) content along the Marion Dufresne route, which cruises more than 300 days per year in the tropical and austral Indian Ocean. This paper presents a first assessment of this GNSS-based IWV retrieval, based on the analysis of 9 months of GNSS raw data acquired along the route of the R/V Marion Dufresne in the Indian Ocean. A first investigation of GNSS raw data collected during the first 5 months of operation has highlighted the bad positioning of the antenna on the R/V that makes it prone to interference. Changing the location of the antenna has been shown to improve the quality of the raw data. Then, ship-borne GNSS-IWV are compared with IWV estimates deduced using more conventional techniques such as European Centre for Medium-range Weather Forecasts (ECMWF) fifth reanalysis (ERA5), ground-launched radiosondes and permanent ground GNSS stations operating close to the route of the R/V Marion Dufresne. The rms difference of 2.79 kg m−2 shows a good match with ERA5 and subsequently improved after the change in location of the GNSS antenna (2.49 kg m−2). The match with ground-based permanent GNSS stations fluctuates between 1.30 and 3.63 kg m−2, which is also shown to be improved after the change in location of the GNSS antenna. However, differences with ground-launched radiosondes still exhibit large biases (larger than 2 kg m−2). Finally, two operational daily routine analyses (at day+1 and day+3) are presented and assessed: the rms of the differences are shown to be quite low (1 kg m−2 for the day+1 analyses, 0.7 kg m−2 for the day+3 analysis), which confirms the quality of these routine analysis. These two routine analyses are intended to provide a continuous monitoring of water vapour above the Indian Ocean and deliver ship-borne IWV with a low latency for the entire scientific community.
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Li, Tuan, Hongping Zhang, Zhouzheng Gao, Xiaoji Niu, and Naser El-sheimy. "Tight Fusion of a Monocular Camera, MEMS-IMU, and Single-Frequency Multi-GNSS RTK for Precise Navigation in GNSS-Challenged Environments." Remote Sensing 11, no. 6 (March 13, 2019): 610. http://dx.doi.org/10.3390/rs11060610.

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Precise position, velocity, and attitude is essential for self-driving cars and unmanned aerial vehicles (UAVs). The integration of global navigation satellite system (GNSS) real-time kinematics (RTK) and inertial measurement units (IMUs) is able to provide high-accuracy navigation solutions in open-sky conditions, but the accuracy will be degraded severely in GNSS-challenged environments, especially integrated with the low-cost microelectromechanical system (MEMS) IMUs. In order to navigate in GNSS-denied environments, the visual–inertial system has been widely adopted due to its complementary characteristics, but it suffers from error accumulation. In this contribution, we tightly integrate the raw measurements from the single-frequency multi-GNSS RTK, MEMS-IMU, and monocular camera through the extended Kalman filter (EKF) to enhance the navigation performance in terms of accuracy, continuity, and availability. The visual measurement model from the well-known multistate constraint Kalman filter (MSCKF) is combined with the double-differenced GNSS measurement model to update the integration filter. A field vehicular experiment was carried out in GNSS-challenged environments to evaluate the performance of the proposed algorithm. Results indicate that both multi-GNSS and vision contribute significantly to the centimeter-level positioning availability in GNSS-challenged environments. Meanwhile, the velocity and attitude accuracy can be greatly improved by using the tightly-coupled multi-GNSS RTK/INS/Vision integration, especially for the yaw angle.
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Hu, Jiahuan, Ding Yi, and Sunil Bisnath. "A Comprehensive Analysis of Smartphone GNSS Range Errors in Realistic Environments." Sensors 23, no. 3 (February 2, 2023): 1631. http://dx.doi.org/10.3390/s23031631.

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Precise positioning using smartphones has been a topic of interest especially after Google decided to provide raw GNSS measurement through their Android platform. Currently, the greatest limitations in precise positioning with smartphone Global Navigation Satellite System (GNSS) sensors are the quality and availability of satellite-to-smartphone ranging measurements. Many papers have assessed the quality of GNSS pseudorange and carrier-phase measurements in various environments. In addition, there is growing research in the inclusion of a priori information to model signal blockage, multipath, etc. In this contribution, numerical estimation of actual range errors in smartphone GNSS precise positioning in realistic environments is performed using a geodetic receiver as a reference. The range errors are analyzed under various environments and by placing smartphones on car dashboards and roofs. The distribution of range errors and their correlation to prefit residuals is studied in detail. In addition, a comparison of range errors between different constellations is provided, aiming to provide insight into the quantitative understanding of measurement behavior. This information can be used to further improve measurement quality control, and optimize stochastic modeling and position estimation processes.
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Qian, Kun, Jian-Guo Wang, and Baoxin Hu. "Novel Integration Strategy for GNSS-Aided Inertial Integrated Navigation." GEOMATICA 69, no. 2 (June 2015): 217–30. http://dx.doi.org/10.5623/cig2015-205.

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The conventional integration mechanism in GNSS (Global Navigation Satellite Systems) aided inertial integrated positioning and navigation system is mainly based on the continuous outputs of the navigation mechanization, the associated error models for navigation parameters, the biases of the inertial measurement units (IMU), and the error measurements. Its strong dependence on the a priori error characteristics of inertial sensors may suffer with the low-cost IMUs, e.g. the MEMS IMUs due to their low and unstable performance. This paper strives for a significant breakthrough in a compact and general integration strategy which restructures the Kalman filter by deploying a system model on the basis of 3D kinematics of a rigid body and performing measurement update via all sensor data inclusive of the IMU measurements. This novel IMU/GNSS Kalman filter directly estimates navigational parameters instead of the error states. It enables the direct use of the IMU's raw outputs as measurements in measurement updates of Kalman filter instead of involving the free inertial navigation calculation through the conventional integration mechanism. This realization makes all of the sensors in a system no longer to be differentiated between core and aiding sensors. The proposed integration strategy can greatly enhance the sustainability of low-cost navigation systems in poor GNSS and/or GNSS denied environment compared to the conventional aided error-state-based inertial navigation integration mechanism. The post-processed solutions are presented to show the success of the proposed multisensor integrated navigation strategy.
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Guo, Lei, Fuhong Wang, Jizhang Sang, Xiaohu Lin, Xuewen Gong, and Wanwei Zhang. "Characteristics Analysis of Raw Multi-GNSS Measurement from Xiaomi Mi 8 and Positioning Performance Improvement with L5/E5 Frequency in an Urban Environment." Remote Sensing 12, no. 4 (February 24, 2020): 744. http://dx.doi.org/10.3390/rs12040744.

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Achieving continuous and high-precision positioning services via smartphone under a Global Navigation Satellite System (GNSS)-degraded environment is urgently demanded by the mass market. In 2018, Xiaomi launched the world’s first dual-frequency GNSS smartphone, Xiaomi Mi 8. The newly added L5/E5 signals are more precise and less prone to distortions from multipath reflections. This paper discusses the characteristics of raw dual-frequency GNSS observations from Xiaomi Mi 8 in urban environments; they are characterized by high pseudorange noise and frequent signal interruption. The traditional dual-frequency ionosphere-free combination is not suitable for Xiaomi Mi 8 raw GNSS data processing, since the noise of the combined measurements is much larger than the influence of the ionospheric delay. Therefore, in order to reasonably utilize the high precision carrier phase observations, a time differenced positioning filter is presented in this paper to deliver continuous and smooth navigation results in urban environments. The filter first estimates the inter-epoch position variation (IEPV) with time differenced uncombined L1/E1 and L5/E5 carrier phase observations and constructs the state equation with IEPV to accurately describe the user’s movement. Secondly, the observation equations are formed with uncombined L1/E1 and L5/E5 pseudorange observations. Then, kinematic experiments in open-sky and GNSS-degraded environments are carried out, and the proposed filter is assessed in terms of the positioning accuracy and solution availability. The result in an open-sky environment shows that, assisted with L5/E5 observations, the root mean square (RMS) of the stand-alone horizontal and vertical positioning errors are about 1.22 m and 1.94 m, respectively, with a 97.8% navigation availability. Encouragingly, even in a GNSS-degraded environment, smooth navigation services with accuracies of 1.61 m and 2.16 m in the horizontal and vertical directions are obtained by using multi-GNSS and L5/E5 observations.
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Ma, Fangwu, Jinzhu Shi, Liang Wu, Kai Dai, and Shouren Zhong. "Consistent Monocular Ackermann Visual–Inertial Odometry for Intelligent and Connected Vehicle Localization." Sensors 20, no. 20 (October 10, 2020): 5757. http://dx.doi.org/10.3390/s20205757.

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The observability of the scale direction in visual–inertial odometry (VIO) under degenerate motions of intelligent and connected vehicles can be improved by fusing Ackermann error state measurements. However, the relative kinematic error measurement model assumes that the vehicle velocity is constant between two consecutive camera states, which degrades the positioning accuracy. To address this problem, a consistent monocular Ackermann VIO, termed MAVIO, is proposed to combine the vehicle velocity and yaw angular rate error measurements, taking into account the lever arm effect between the vehicle and inertial measurement unit (IMU) coordinates with a tightly coupled filter-based mechanism. The lever arm effect is firstly introduced to improve the reliability for information exchange between the vehicle and IMU coordinates. Then, the process model and monocular visual measurement model are presented. Subsequently, the vehicle velocity and yaw angular rate error measurements are directly used to refine the estimator after visual observation. To obtain a global position for the vehicle, the raw Global Navigation Satellite System (GNSS) error measurement model, termed MAVIO-GNSS, is introduced to further improve the performance of MAVIO. The observability, consistency and positioning accuracy were comprehensively compared using real-world datasets. The experimental results demonstrated that MAVIO not only improved the observability of the VIO scale direction under the degenerate motions of ground vehicles, but also resolved the inconsistency problem of the relative kinematic error measurement model of the vehicle to further improve the positioning accuracy. Moreover, MAVIO-GNSS further improved the vehicle positioning accuracy under a long-distance driving state. The source code is publicly available for the benefit of the robotics community.
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Chang, Guobin, Chao Chen, Yuanxi Yang, and Tianhe Xu. "Tikhonov Regularization Based Modeling and Sidereal Filtering Mitigation of GNSS Multipath Errors." Remote Sensing 10, no. 11 (November 14, 2018): 1801. http://dx.doi.org/10.3390/rs10111801.

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In Global Navigation Satellite System (GNSS) relative positioning applications, multipath errors are non-negligible. Mitigation of the multipath error is an important task for precise positioning and it is possible due to the repeatability, even without any rigorous mathematical model. Empirical modeling is required for this mitigation. In this work, the multipath error modeling using carrier phase measurement residuals is realized by solving a regularization problem. Two Tikhonov regularization schemes, namely with the first and the second order differences, are considered. For each scheme, efficient numerical algorithms are developed to find the solutions, namely the Thomas algorithm and Cholesky rank-one update algorithm for the first and the second differences, respectively. Regularization parameters or Lagrange multipliers are optimized using the bootstrap method. In experiment, data on the first day are processed to construct a multipath model for each satellite (except the reference one), and then the model is used to correct the measurement on the second day, namely following the sidereal filtering approach. The smoothness of the coordinates calculated using the corrected measurements is improved significantly compared to those using the raw measurement. The efficacy of the proposed method is illustrated by the actual calculation.
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Yun, Jeonghyeon, Cheolsoon Lim, and Byungwoon Park. "Inherent Limitations of Smartphone GNSS Positioning and Effective Methods to Increase the Accuracy Utilizing Dual-Frequency Measurements." Sensors 22, no. 24 (December 15, 2022): 9879. http://dx.doi.org/10.3390/s22249879.

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Xiaomi Mi8 with a Broadcom BCM47755 chip, an Android smartphone that supports multi-constellation (GPS, GLONASS, Galileo, BeiDou, and QZSS) and dual-frequency (L1/E1 and L5/E5), was launched in May 2018. Unlike previously released smartphones, it was technically expected to provide robust precise positioning with a fast ambiguity resolution, which led many researchers to be overly optimistic about the applicability of high-accuracy techniques such as real-time kinematic (RTK) systems and precise point positioning (PPP) of smartphones. The global navigation satellite system (GNSS) raw measurement quality of Android smartphones is, however, inherently far lower than that of general GNSS receivers due to their structure, which accordingly makes it difficult for them to be realized. Considering inherent limitations of smartphones such as low-quality antenna, frequent cycle slips, and the duty cycle, a practical strategy including L5 measurements, pseudo-range corrections for L5, and a weighting method is proposed in this paper. The results show that the proposed methods of L5 differential GNSS (DGNSS) and Doppler-based filtering can guarantee a positioning accuracy of 1.75 m horizontally and 4.56 m vertically in an Android device, which is comparable to the performance of commercial low-cost receivers.
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Xu, Hongfu, Haiyong Luo, Zijian Wu, Fan Wu, Linfeng Bao, and Fang Zhao. "Towards Predicting the Measurement Noise Covariance with a Transformer and Residual Denoising Autoencoder for GNSS/INS Tightly-Coupled Integrated Navigation." Remote Sensing 14, no. 7 (March 31, 2022): 1691. http://dx.doi.org/10.3390/rs14071691.

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The tightly coupled navigation system is commonly used in UAV products and land vehicles. It adopts the Kalman filter to combine raw satellite observations, including the pseudorange, pseudorange rate and Doppler frequency, with the inertial measurements to achieve high navigational accuracy in GNSS-challenged environments. The accurate estimation of measurement noise covariance can ensure the quick convergence of the Kalman filter and the accuracy of the navigation results. Existing tightly coupled integrated navigation systems employ either constant noise covariance or simple noise covariance updating methods, which cannot accurately reflect the dynamic measurement noises. In this article, we propose an adaptive measurement noise estimation algorithm using a transformer and residual denoising autoencoder (RDAE), which can dynamically estimate the covariance of measurement noise. The residual module is used to solve the gradient degradation problem. The DAE is adopted to learn the essential characteristics from the noisy ephemeris data. By introducing the attention mechanism, the transformer can effectively learn the time and space dependency of long-term ephemeris data, and thus dynamically adjusts the noise covariance with the predicted factors. Extensive experimental results demonstrate that our method can achieve sub-meter positioning accuracy in the outdoor open environment. In a GNSS-degraded environment, our proposed method can still obtain about 3 m positioning accuracy. Another test on a new dataset also confirms that our proposed method has reasonable robustness and adaptability.
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Jing, Ce, Guanwen Huang, Qin Zhang, Xin Li, Zhengwei Bai, and Yuan Du. "GNSS/Accelerometer Adaptive Coupled Landslide Deformation Monitoring Technology." Remote Sensing 14, no. 15 (July 23, 2022): 3537. http://dx.doi.org/10.3390/rs14153537.

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Global Navigation Satellite System (GNSS) positioning technology has become the most effective method for real-time three-dimensional landslide monitoring. However, the GNSS observation signal is easily affected by the presence of a complex landslide environment with high occlusion and strong interference, in which case its accuracy and reliability cannot meet the requirements of landslide deformation monitoring. Although the accelerometers have strong autonomous working capacities and can complement the GNSS landslide monitoring technology, regular GNSS/accelerometer coupled deformation monitoring technology relies on high-quality GNSS measurement information in order to obtain high-precision accelerometer-reckoned results, derived by accurately estimating the baseline shift error (BSE). When the GNSS signal suffers severe interference, the GNSS monitoring error will be partially absorbed by the BSE component after Kalman filtering, resulting in the divergence of the deformation solution. In this study, an abnormal observation variance inflation model was used to process the simulated landslide monitoring data (GNSS and accelerometer raw observation) for three typical scenes—GNSS signal normally locked, signal partially lost, and short-term interruption. The results were as follows: (1) When the GNSS signal was normally locked, the accuracy was comparable to that of the coupled solution employing an accelerometer (the Root Mean Square (RMS) values in the East (E), North (N) and Upward (U) directions were 0.11 cm, 0.33 cm, and 0.30 cm, respectively). (2) When the GNSS signal was partially lost, the accelerometer could effectively suppress the low-precision float solution of the GNSS, but here, the accuracy of the coupled solution would also decrease with the duration of the floats (the RMS values were E—1.21 cm, N—0.31 cm, and U: 0.58—cm, respectively, when the floats lasted for 10 s, and increased to E—3.09 cm, N—0.39 cm, and U—1.14 cm when they lasted for 20 s, wherein E was the main simulated sliding direction). (3) When the GNSS signal was interrupted for a short time, the accuracy of the coupled solution gradually decreased during continuous interruption, and decreased more quickly during the sliding period of the landslide (when the interruption persisted for 10 s, the RMS values in the simulated landslide stability period were E—0.61 cm, N—0.24 cm, and U—0.25 cm, respectively, while in the simulated landslide sliding period they reached E—4.10 cm, N—6.84 cm, and U—2.30 cm). However, raw observations of the accelerometer could still effectively be used to assist in identifying the real state of the landslide, thereby providing auxiliary information pertinent to early landslide disaster warning.
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Book chapters on the topic "GNSS raw measurement"

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Seo, Ki-Yeol, Won-Seok Jang, and Young-Ki Kim. "Detection Method of Radio Frequency Interference Using Raw Measurement of Multi-GNSS Receivers." In Advances in Intelligent Systems and Computing, 55–62. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-05570-1_6.

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Johann, Felix, David Becker, Matthias Becker, and E. Sinem Ince. "Multi-Scenario Evaluation of the Direct Method in Strapdown Airborne and Shipborne Gravimetry." In International Association of Geodesy Symposia. Berlin, Heidelberg: Springer Berlin Heidelberg, 2020. http://dx.doi.org/10.1007/1345_2020_127.

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AbstractIn recent years, it was shown that the quality of strapdown airborne gravimetry using a navigation-grade strapdown inertial measurement unit (IMU) could be on par with “classical” airborne gravimeters as the 2-axis stabilized LaCoste and Romberg S-type gravimeter. Basically, two processing approaches exist in strapdown gravimetry. Applying the indirect method (also referred to as “inertial navigation approach” or “one-step approach”), all observations – raw GNSS observations or position solutions, IMU specific force and angular rate measurements – are combined in a single Kalman Filter. Alternatively, applying the direct method (also referred to as “accelerometry approach” or “cascaded approach”), GNSS position solutions are numerically differentiated twice to get the vehicle’s kinematic acceleration, which is then directly removed from the IMU specific force measurement in order to obtain gravity. In the scope of this paper, test runs for the application of strapdown airborne and shipborne gravimetry are evaluated using an iMAR iNAV-RQH-1003 IMU. Results of the direct and the indirect methods are compared to each other. Additionally, a short introduction to the processing scheme of the Chekan-AM gravimeter data is given and differences between Chekan-AM and strapdown results of the shipborne campaigns are analysed. Using the same data set, the cross-over residuals suggest a similar accuracy of 0.39 mGal for the Chekan-AM and 0.41 mGal for the adjusted strapdown results (direct method).
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Moeller, Gregor. "Nanosatellites: The Next Big Chapter in Atmospheric Tomography." In Inverse Problems - Recent Advances and Applications [Working Title]. IntechOpen, 2022. http://dx.doi.org/10.5772/intechopen.108522.

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Nanosatellite technology opens up new possibilities for earth observation. In the next decade, large satellite constellations will arise with hundreds, up to thousand of satellites in low earth orbit. A number of satellites will be equipped with rather low-cost sensors, such as GNSS receivers, suited for atmospheric monitoring. However, the future evolution in atmospheric science leans not only on densified observing systems but also on new, more complex analysis methods. In this regard, tomographic principles provide a unique opportunity for sensor fusion. The difficulty in performing the conversion of integral measurements into 3D images is that the signal ray path is not a straight line and the number of radio sources and detectors is limited with respect to the size of the object of interest. Therefore, the inverse problem is either solved linearly or iterative nonlinear. In this chapter, an overview about the individual solving techniques for the tomographic problem is presented, including strategies for removing deficiencies of the ill-posed problem by using truncated singular value decomposition and the L-curve technique. Applied to dense nanosatellite formations, a new quality in the reconstruction of the 3D water vapor distribution is obtained, which has the potential for leading to further advances in atmospheric science.
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Su, Jingyao, and Steffen Schön. "Bounding the Residual Tropospheric Error by Interval Analysis." In International Association of Geodesy Symposia. Berlin, Heidelberg: Springer Berlin Heidelberg, 2022. http://dx.doi.org/10.1007/1345_2022_184.

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AbstractGNSS integrity monitoring requires proper bounding to characterize all ranging error sources. Unlike classical approaches based on probabilistic assumptions, our alternative integrity approach depends on deterministic interval bounds as inputs. The intrinsically linear uncertainty propagation with intervals is adequate to describe remaining systematic uncertainty, the so-called imprecision. In this contribution, we make a proposal on how to derive the required intervals in order to quantify and bound the residual error for empirical troposphere models, based on the refined sensitivity analysis via interval arithmetic. We evaluated experimentally the Saastamoinen model with (i) a priori ISO standard atmosphere, and (ii) on-site meteorological measurements from IGS and Deutscher Wetterdienst (DWD) stations as inputs. We obtain consistent and complete enclosure of residual ZPD errors w.r.t IGS ZPD products. Thanks to the DWD dense network, interval maps for meteorological parameters and residual ZPD errors are generated for Germany as by-products. These experimental results and products are finally validated, taking advantage of the high-quality tropospheric delays estimated by the Vienna Ray Tracer. Overall, the results indicate that our strategy based on interval analysis successfully bounds tropospheric model uncertainty. This will contribute to a realistic uncertainty assessment of GNSS-based single point positioning.
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Conference papers on the topic "GNSS raw measurement"

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Jau, Pei-Hung, and ShengYu Huang. "GNSS Raw Measurement on the Latest MediaTek Platform." In 30th International Technical Meeting of The Satellite Division of the Institute of Navigation (ION GNSS+ 2017). Institute of Navigation, 2017. http://dx.doi.org/10.33012/2017.15113.

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Fu, Guoyu (Michael), Mohammed Khider, and Frank van Diggelen. "Android Raw GNSS Measurement Datasets for Precise Positioning." In 33rd International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2020). Institute of Navigation, 2020. http://dx.doi.org/10.33012/2020.17628.

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Lu, Yangwei, Shengyue Ji, Wu Chen, and Zhenjie Wang. "Assessing the Performance of Raw Measurement from Different Types of Smartphones." In 31st International Technical Meeting of The Satellite Division of the Institute of Navigation (ION GNSS+ 2018). Institute of Navigation, 2018. http://dx.doi.org/10.33012/2018.15881.

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Dimitrov, Nikolay, and Mila Atanasova. "GEODETIC DATABASE FOR MONITORING OF GEODYNAMIC PROCESSES IN THE REGION OF SOFIA AND SOUTHWESTERN BULGARIA." In 22nd SGEM International Multidisciplinary Scientific GeoConference 2022. STEF92 Technology, 2022. http://dx.doi.org/10.5593/sgem2022/2.1/s09.28.

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This paper covers the activities of creating a database for the study of modern crustal movements in the region of Sofia and Southwestern Bulgaria, including data from GNSS measurements of the geodynamic network and the results of their processing and analysis, data from permanent stations and archive data from Synthetic-aperture radar, processing and obtaining interferometric images. The local archive of GNSS measurement includes: manual sketches of the location of the points; log sheets of the measurement with information for the height of the antenna, start and end time of measurement and operator name; raw GNSS measurements and data in RINEX format. Data from permanent stations are extracted from external repositories such as IGS or SOPAC are also included in the archive. The local archive of interferometric images contains scenes from the Sentinel-1 satellite downloaded from the repository of the European Space Agency's Science Center. Data for a digital terrain model are extracted from external sources, such as SRTM or ASTER repositories are also included. In order of the envisaged integration with GNSS data, it should be borne in mind that both types of data must be in the same reference system. Measurement campaigns in the geodetic network are carried out in a certain period of time and these periods are used as reference, which allows comparisons to the information extracted from interferometric images. The available experience shows that there is a good correlation between the two types of data. The processing and analysis of the obtained results is another important part of the local archive. The database thus created contains important information and allows for a more accurate analysis of ongoing geodynamic processes in the study area and obtaining reliable information for better regional planning from local authorities.
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Yang, Ryan K. Y., Shi Xian Yang, Gary Hau, Chia Wei Sung, and Hongtao Yu. "Single-Chip Delivers Multi-Band Multi-GNSS Raw Measurement and Built-In RTK Engine for Mass Market Applications." In 2019 International Technical Meeting of The Institute of Navigation. Institute of Navigation, 2019. http://dx.doi.org/10.33012/2019.16680.

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Cho, Bogeun, Bu-Gyeom Kim, Jong Heon Kim, Changdon Kee, Sunkyoung Yu, O.-Jong Kim, and Jungbeom Kim. "Assessment of the Quality of Raw Measurement from Samsung SOC GNSS Chip and Analysis of Positioning Accuracy Using PPP." In 2022 International Technical Meeting of The Institute of Navigation. Institute of Navigation, 2022. http://dx.doi.org/10.33012/2022.18181.

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VERHEYDE, Thomas, Antoine BLAIS, Christophe MACABIAU, and Francois-Xavier MARMET. "Analyzing Android GNSS Raw Measurements Flags Detection Mechanisms for Collaborative Positioning in Urban Environment." In 2020 International Conference on Localization and GNSS (ICL-GNSS). IEEE, 2020. http://dx.doi.org/10.1109/icl-gnss49876.2020.9115564.

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Zhou, Xiao, Hong Li, and Mingquan Lu. "A GNSS Spoofing Detection Method Based on Raw GNSS/IMU/Camera Measurements." In 2023 International Technical Meeting of The Institute of Navigation. Institute of Navigation, 2023. http://dx.doi.org/10.33012/2023.18631.

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Miralles, Damian, Maxim S. Moghadam, and Dennis M. Akos. "GNSS Threat Monitoring and Reporting with the Android Raw GNSS Measurements and STRIKE3." In 32nd International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2019). Institute of Navigation, 2019. http://dx.doi.org/10.33012/2019.16984.

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van Diggelen, Frank. "Google Analysis Tools for GNSS Raw Measurements, g.co/GNSSTools." In 30th International Technical Meeting of The Satellite Division of the Institute of Navigation (ION GNSS+ 2017). Institute of Navigation, 2017. http://dx.doi.org/10.33012/2017.15172.

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