The Multi-GNSS Experiment (MGEX) of the International GNSS Service (IGS) - Achievements, prospects and challenges

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The Antarctica component of postglacial rebound model ICE-6G_C (VM5a) based on GPS positioning, exposure age dating of ice thicknesses, and relative sea level histories

In this paper, we propose a novel localization methodology to enhance the accuracy from two aspects, i.e., adapting to the uncertain noise of microelectromechanical system-based inertial navigation system (MEMS-INS) and accurately predicting INS errors. First, an interacting multiple model (IMM)-based sequential two-stage Kalman filter is proposed to fuse the information of MEMS-INS, global positioning system (GPS), and in-vehicle sensors. Three bias filters are built with different covariance matrices to cover a wide range of noise characteristics. Then, IMM algorithm provides a soft switching among the three bias filters to adapt to the uncertain noise of MEMS-INS. Further, an elaborate predictor is developed to accurately predict INS errors during GPS outages. The elaborate predictor comprises an online trained autoregressive integrated moving average (ARIMA) model and an offline trained extreme learning machine (ELM) model. The ARIMA model is designed to predict the basic accumulation process of INS errors, while the ELM model is designed to correct the errors caused by the changes of noise characteristics. Thus, the INS errors can be properly compensated when GPS observations are not available. In all, the proposed localization methodology can achieve accurate performance when facing uncertain noises of MEMS-INS and GPS outages simultaneously. To verify the effectiveness of the proposed methodology, road test experiments with various driving scenarios were performed. The experimental results illustrate the feasibility and effectiveness of the proposed methodology.

Enhancing Localization Accuracy of MEMS-INS/GPS/In-Vehicle Sensors Integration During GPS Outages

The Fourth Industrial Revolution (Industry 40) is reshaping the construction industry and bringing it into an intelligent construction era Emerging technologies, such as the Building Information Modelling, Internet of Things, big data, cloud computing, and artificial intelligence, have penetrated into all stages of the building life cycle and play a significant role However, the major issue of intelligent construction is integrating multiple technologies to create more potential opportunities rather than their fragmented application Considering the various special characteristics of the construction industry and the high heterogeneity of these technologies, their integration in the construction industry is challenging and requires in-depth investigations This paper summarizes the Industry 40-related technologies involved in the construction industry based on an analysis of the characteristics of the industry Further, this study presents a framework of a cyber-physical system to integrate these technologies and improve the overall capabilities of construction organization and management A case study of the Xiong'an citizen service center is introduced to verify the technological feasibility and preliminary implementation effect of the proposed framework As forward-looking research, the significance of this paper may also to inspire more efforts in this field

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Integration of Industry 4.0 Related Technologies in Construction Industry: A Framework of Cyber-Physical System

The update of the Android system and the emergence of the dual-frequency GNSS chips enable smartphones to acquire dual-frequency GNSS observations. In this paper, the GPS L1/L5 and Galileo E1/E5a dual-frequency PPP (precise point positioning) algorithm based on RTKLIB and GAMP was applied to analyze the positioning performance of the Xiaomi Mi 8 dual-frequency smartphone in static and kinematic modes. The results showed that in the static mode, the RMS position errors of the dual-frequency smartphone PPP solutions in the E, N, and U directions were 21.8 cm, 4.1 cm, and 11.0 cm, respectively, after convergence to 1 m within 102 min. The PPP of dual-frequency smartphone showed similar accuracy with geodetic receiver in single-frequency mode, while geodetic receiver in dual-frequency mode has higher accuracy. In the kinematic mode, the positioning track of the smartphone dual-frequency data had severe fluctuations, the positioning tracks derived from the smartphone and the geodetic receiver showed approximately difference of 3–5 m.

https://www.mdpi.com/1424-8220/19/9/2189/pdf

Precise Point Positioning Using Dual-Frequency GNSS Observations on Smartphone

A review of the research conducted until present on the subject of Global Navigation Satellite System (GNSS) hardware-induced phase and code biases is here provided. Biases in GNSS positioning occu ...

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Review of code and phase biases in multi-GNSS positioning

Geographic capabilities and limitations of Industry Foundation Classes

The performance of a GPS/INS integration system is greatly determined by the ability of stand-alone INS system to determine position and attitude within GPS outage. The positional and attitude precision degrades rapidly during GPS outage due to INS sensor errors. With advantages of low price and volume, the Micro Electrical Mechanical Sensors (MEMS) have been wildly used in GPS/INS integration. Moreover, standalone MEMS can keep a reasonable positional precision only a few seconds due to systematic and random sensor errors. General stochastic error sources existing in inertial sensors can be modelled as (IEEE STD 647, 2006) Quantization Noise, Random Walk, Bias Instability, Rate Random Walk and Rate Ramp. Here we apply different methods to analyze the stochastic sensor errors, i.e. autoregressive modelling, Gauss-Markov process, Power Spectral Density and Allan Variance. Then the tests on a MEMS based inertial measurement unit were carried out with these methods. The results show that different methods give similar estimates of stochastic error model parameters. These values can be used further in the Kalman filter for better navigation accuracy and in the Doppler frequency estimate for faster acquisition after GPS signal outage.

/pdf/stochastic-modelling-and-analysis-of-imu-sensor-errors-3mys6mjgcs.pdf

Stochastic modelling and analysis of IMU sensor errors

Terrestrial laser scanning technology has developed rapidly in recent years and has been used in various applications but mainly in the surveying of different buildings and historical monuments. Th ...

/pdf/geodetic-monitoring-of-bridge-deformations-occurring-during-2rq4hnb8ka.pdf

Geodetic Monitoring of Bridge Deformations Occurring During Static Load Testing

The sea boundary between two states with opposite or adjacent coasts is defined by the median line between baselines realized on shore lines. As defined in UNCLOS (United Nations Convention on the Law of the Sea), every point of the median line must be equidistant from the nearest points on the baselines from which the breadth of the territorialseas of each ofthe two states is measured. The coordinates of breakpoints of the median line are computed based on known coordinates of baseline points on the shores. What is the precision of computed coordinates? Here we study the error propagation on two reference surfaces: (1) plane and (2) sphere. Moreover, four approaches to breakpoint determination are investigated. Algorithms for computation of the covariance matrix of median line breakpoints both on sphere and plane were developed.

Milan Horemuz

Papers

Review of code and phase biases in multi-GNSS positioning

Geographic capabilities and limitations of Industry Foundation Classes

Stochastic modelling and analysis of IMU sensor errors

Geodetic Monitoring of Bridge Deformations Occurring During Static Load Testing

Error Calculation in Maritime Delimitation between States with Opposite or Adjacent Coasts