In order to meet the requirements of high-precision vehicle positioning in complex scenes, an observation noise adaptive robust GNSS/MIMU tight fusion model based on the gain matrix is proposed considering static zero speed, non-integrity, attitude, and odometer constraint models. In this model, the robust equivalent gain matrix is constructed by the IGG-Ⅲ method to weaken the influence of gross error, and the on-line adaptive update of observation noise matrix is carried out according to the change of actual observation environment, so as to improve the solution performance of filtering system and realize high-precision position, attitude and velocity measurement when GNSS signal is unlocked. A real test on a road over 600km demonstrates that, in about 100km shaded environment, the fixed rate of GNSS ambiguity resolution in the shaded road is 10% higher than that of GNSS only ambiguity resolution. For all the test, the positioning accuracy can reach the centimeter level in an open environment, better than 0.6m in the tree shaded environment, better than 1.5m in the three-dimensional traffic environment, and can still maintain a positioning accuracy of 0.1m within 10s when the satellite is unlocked in the tunnel scene. The proposal and verification of the algorithm model show that low-cost MIMU equipment can still achieve high-precision positioning when there are scene feature constraints, which can meet the problem of high-precision vehicle navigation and location in the urban complex environment.

The Global Navigation Satellite Systems (GNSS) broadcast radio signals are continuously at two or more frequencies in the L-band, and the multipath signals from sea surface recorded by off-the-shelf geodetic receivers have been demonstrated they can be used to estimate sea level, using a technology called GNSS multipath reflectometry (GNSS-MR). Before proceeding to estimate reflection parameters, the azimuth range and elevation angle range are needed to be defined, as only with suitable azimuths and elevation angles the sensing zones can be guaranteed on water. So, this study presents an angle dependence analysis method to jointly select the azimuth range and elevation angle range based on wavelet analysis which can describe the non-stationary power of different sinusoidal oscillations changed with elevation angle. The key of this method is to use one grid model to screen the spectrum power of multipath oscillation on different elevation angles and azimuths in this work. Then the elevation angles and the azimuths can be determined by searching grids with greater power. The GPS and GLONASS data of two Multi-GNSS Experiment (MGEX) stations named BRST and MAYG was analyzed and used to retrieve. Firstly, the angle dependence analysis was carried out to determine the elevation range and azimuth range. Secondly, the sea levels were retrieved from individual signals. Finally, the retrievals of individual signals are combined to form a 10-min sea level retrieval series. The RMSEs of the combined retrievals are both less than 15cm. The results show the effectiveness of the selection of angle range based on the angle dependence analysis method.

Urban public infrastructure is an important basis for urban development. It is of great significance to deepen the research on intelligent management and control of urban public infrastructure. Spatio-temporal information contains the law of state evolution of urban public infrastructure, which is the information base of intelligent control of infrastructure. Due to the needs of operation management and emergency response, efficient sharing and visualization of spatio-temporal information are important research contents of comprehensive management and control of urban public infrastructure. On the basis of summarizing the theoretical research and application in recent years, the basic methods and current situation of the acquisition and analysis of spatio-temporal information, the forecast and early warning, and the intelligent control of urban public infrastructure are reviewed in this paper.

Using GNSS-R technology for remote sensing of surface parameters has become a new trend in the field of remote sensing. With the rapid development of GNSS-R technology, GNSS-R simulation has become one of the new hot spots. Now the researches of the GNSS-R simulation are all the simulations that consider a single star or a single frequency point, and in actual applications, the signal captured by the receiver is often the reflected signals of multiple stars. In view of this situation, from the perspective of multi-satellite simulation, this paper gives the model of GNSS-R multi-satellite ocean simulation on the basis of analyzing the remote sensing principle, reflection signal model and simulation principle of GNSS-R technology. Based on the GNSS-R multi-satellite ocean simulation model and the fast parallel computing capability of GPU, the GNSS-R multi-satellite ocean simulator was designed. Finally, the direct and reflected signals generated by the GNSS-R multi-satellite simulator were tested and verified. The results show that the positioning result of the direct signal meets the positioning accuracy requirements; The delay-related power results obtained from the simulated two-satellite reflected signals processing are in good agreement with the theoretical model, and the correlation coefficients are all above 0.99; The generated signals are used for GNSS-R height measurement technology, the height measurement error is about 1.4~1.8m, which is in line with the accuracy of the C/A code ranging receiver; And the parallel operation of the GPU for multi-satellite simulation calculation is 800—900 times higher than the traditional CPU calculation. It proves that the proposed model and the designed simulator are feasible and accurate.

Snow cover is one of the important components of land cover, and it is necessary to accurately monitor the depth and coverage of snow cover. Using the GPS signal receiver data and the basic principle of snow depth detection based on GPS-MR technology, the snow depth of the three sites on the Greenland PBO network GLS1, GLS2, and GLS3 from 2012 to 2018 was obtained. The inversion snow depth is affected by site drift, which is a quite difference from the measured snow depth. Combined with the stable reference point, the velocity field distribution of Greenland Island and the U-direction component change value of the station can be obtained through GAMIT calculation. By analyzing the glacial flow and U-direction component, the influence of the site drift on the snow depth was deducted, and finally compared the corrected inversion snow depth and measured snow depth found that the two were better than before the correction, the results were significantly improved, and the consistency was good. The analysis of the experimental results showed that in extremely cold areas such as Greenland Island, affected by glaciers, the continuous, real-time, high-time resolution snow depth around the measured station obtained by ground-based GPS tracking stations has a large gap with the measured snow depth value, and the gap will gradually increase with time. By deducting the impact of glacier drift, the trend of the two is the same and the consistency is good. The correctness and feasibility of the application of ground-based GPS snow cover theory in the polar area further expand the application scope and practical value of ground-based GPS in snow monitoring.

Tunnel deformation monitoring is a crucial task to evaluate tunnel stability during the metro operation period. Terrestrial Laser Scanning (TLS) can collect high density and high accuracy point cloud data in a few minutes as an innovation technique, which provides promising applications in tunnel deformation monitoring. Here, an efficient method for extracting tunnel cross-sections and convergence analysis using dense TLS point cloud data is proposed. First, the tunnel orientation is determined using principal component analysis (PCA) in the Euclidean plane. Two control points are introduced to detect and remove the unsuitable points by using point cloud division and then the ground points are removed by defining an elevation value width of 0.5 m. Next, a z-score method is introduced to detect and remove the outlies. Because the tunnel cross-section’s standard shape is round, the circle fitting is implemented using the least-squares method. Afterward, the convergence analysis is made at the angles of 0°, 30° and 150°. The proposed approach’s feasibility is tested on a TLS point cloud of a Nanjing subway tunnel acquired using a FARO X330 laser scanner. The results indicate that the proposed methodology achieves an overall accuracy of 1.34mm, which is also in agreement with the measurements acquired by a total station instrument. The proposed methodology provides new insights and references for the applications of TLS in tunnel deformation monitoring, which can also be extended to other engineering applications.

Without prior knowledge, the steering vector estimation error of the desired signal will deteriorate the beamforming performance to some extent.To solve this problem, a space-time blind wideband beamforming algorithm based on the uncertainty set is proposed.First of all, based on the space-time filtering model, the spherical constraint set is designed according to the uncertainty of the estimation error of the space-time steering vector. Then, the method of wideband beamforming under multi-constraints in the frequency domain is derived, and the calculation of parameters of steering vector estimation error and loading factor are given in detail. Finally,a blind broadband beamforming algorithm combined with the CAB algorithm is proposed. The improvement of the output signal-to-noise ratio is quantitatively analyzed by computer simulation to verify the correctness and robustness of the algorithm.

The expansion of research and applications of Global Navigation Satellite Systems (GNSS) has revealed the information of reflecting surface in inherent multipath errors. GNSS signals, usually used to measure position, have been demonstrated that they can be used to retrieve water properties including water level, soil moisture, snow depth, and vegetation water content, which are important for climate analysis and water resources monitoring. Reflected GNSS signals with different azimuths can carry information of the corresponding reflecting zone, which means every reflected signal has distinct “signal-to-noise ratio (SNR) characteristics” influenced by specific reflecting zones—and the parameter named “Reflector Height (RH)” deduced from SNR frequency is focused on in this study. Thus, after interpolation of a series of reflector height by coordinates of the footprint, products describing highly detailed terrain over a reflecting footprint can be produced. Data of three GNSS sites in EarthScope Plate Boundary Observatory, named P025, P351 and P101, was used to evaluate the terrain after calculating the terrain slopes and correcting the footprint following the slopes. A comparison of the results with a digital elevation model (DEM) showed that it is possible to retrieve terrain by GNSS-Interferometric Reflectometry (GNSS-IR); and the comparison with terrain slopes from DEMs in previous research also validated its potential.