Yangyang Li

Accurate vehicle localization is essential for ensuring operational safety in cooperative vehicular systems. Nevertheless, maintaining positioning accuracy in dynamic and signal-blocked environments is a critical technical challenge. While cooperative positioning methods exploit spatial correlations among vehicular nodes and swarm data fusion to enhance positioning reliability, their performance remains susceptible to outliers. Under the cooperative positioning framework, this study presents the robust centralized and distributed positioning using hybrid Global Navigation Satellite System (GNSS) and Ultra-wideband (UWB) measurements. The proposed centralized positioning (CP) establishes geometric constraints through intervehicle UWB ranging measurements while incorporating GNSS pseudo-range observations for swarm position estimation. In contrast, the distributed positioning (DP) implements a two-step estimation where the results from GNSS single point positioning are taken as a prior information to achieve refined solutions together with the UWB measurements. To address outlier susceptibility, both methods further integrate robust estimation with the IGG III weight function, enabling effective outlier detection and mitigation. To demonstrate the performance of robust cooperative positioning, semisimulation experiments are conducted with and without robust estimation. The results reveal dm-level’s relative positioning accuracy is achieved using both cooperative positioning methods. The robust DP represents 55.2% accuracy improvement compared to robust CP while reducing computational time cost by 28.9%, and achieves 51.8% enhancement in accuracy against DP without robust estimation. Besides, optimal hybrid GNSS/UWB performance requires UWB ranging noise below 0.5 m, and comparative analyses under varying noise conditions confirm the robust DP sustains positioning accuracy better than 0.5 m across noise scenarios below 0.5 m.

1. Accurate, reliable, and real-time high-rate relative positioning is a prerequisite for safe and effective work between two moving platforms. The Global Navigation Satellite System (GNSS) has bee...

The micro-electro-mechanical system (MEMS), with its small size, low cost and low power, has attracted widespread attentions in recent year, and the initial alignment of the inertial measurement unit (IMU) is an indispensable step before the navigation computation. In the traditional alignment methods with only the velocity measurements, the yaw angle is subject to divergence under low dynamic scenarios, and the measurements are also susceptible to the external environments. Furthermore, the navigation frame (nframe) is usually chosen as the reference frame. Although intuitive, it is more computationally complex compared with the earth-centered-earth-fixed frame (eframe). Therefore, to improve the alignment performance and computational efficiency, a robust MEMS IMU initial alignment method with yaw assistance under theeframe is proposed. In this method, to address the issues of the yaw divergency and computational inefficiency in the traditional methods, the relationship between the yaw angle, derived from dual-antenna global navigation satellite system baseline solution, and IMU error is derived and incorporated into the state space model witheframe as the reference frame. Besides, a robust extended Kalman filtering is adopted to further improve the system’s performance of outlier resistance, whose robust factors are determined by the Institute of Geodesy and Geophysics Ⅲ model. To validate the performance of the proposed method, two field experiments with the velocity of about 0.7 m s⁻¹and 1.5 m s⁻¹was conducted, and the collected data were processed for contrastive analysis. The results show that the effectiveness of the proposed method at suppressing yaw angle divergence and improving the alignment accuracy by resisting outliers under low dynamic scenarios compared with the traditional method. Moreover, the elapsed time is reduced by about 11% when the reference frame in data processing is switched from thenframe to theeframe.

The rapid development of unmanned aerial vehicles (UAVs) in recent years has promoted their application in various fields, such as precise agriculture, formation flight, etc. In these applications, the accurate and reliable real-time position and attitude determination between each moving device in the same platform system are the key issue for safe and effective cooperative works. In traditional ways, static reference stations should be set up near the platform to keep the stable position datum of the platform system. In this paper, we abandoned the static stations and expected to achieve stable position datums with the platform system itself. To achieve this goal, we proposed an improved method based on both the Global Positioning System (GPS)/Beidou Navigation Satellite System (BDS) data and the inertial navigation system (INS) data to obtain precise positions of the moving devices. The time-differenced carrier phase (TDCP) was used to get the position variations and update the positions over time, and then, the INS data was integrated to further improve the accuracy and reliability of the updated positions; thus, this method is denoted as the TDCP/INS method. To evaluate the performance of this method and compare it with the traditional single-point positioning (SPP) method and the Kalman filtered SPP (KFSPP) method, a field vehicle experiment was conducted, and the position results achieved from these three methods were compared with those from the tightly combined real-time kinematic positioning (RTK)/INS method, where centimeter-level accuracy was obtained and regarded as the reference. The quantitative analysis where the position variations were evaluated and the qualitative analysis where the vehicle trajectories in three typical urban driving scenarios were discussed were both made for the three methods. The numerical results showed that the accuracy of the position variations from the SPP, KSPP, and TDCP methods was at the meter level, while that from the TDCP/INS method improved to the centimeter level, and the accuracies were 1.9 cm, 2.9 cm, and 3.1 cm in the east, north, and upward directions. The trajectory results also demonstrated a perfect consistency of the driving positions between the TDCP/INS method and the reference. As a contrast, the trajectories from the SPP and KFSPP methods had frequent jumps or sways when the vehicle drove along a large, curved road, turned at a crossroad, and passed under an urban viaduct.

The multipath error is considered to be the most limiting factor for high precision positioning applications. The sidereal filtering (SF) method can be used to mitigate the multipath error in the observation domain, and it has been successfully applied in the multipath mitigation in global positioning systems (GPS) and regional BeiDou navigation satellite systems (BDS2). However, there are few reports on the SF method in other systems. The performance of the SF method relies on the explicit orbit repeat periods of satellites in diverse systems or even different types of constellations. It is therefore inconvenient to utilize the SF method for multi-GNSS multipath error mitigation. Alternatively, a space domain multipath error reduction method, which establishes the multi-point hemispherical grid model (MHGM) using the residuals of the double-differenced carrier phase observations in the ambiguity-fixed period, has been modified. It is an integrated model for multi-GNSS, without considering the diversity of different systems and constellations. To compare the performance of MHGM and SF from a multi-GNSS point of view, the determination method of orbit repeat periods via the broadcast ephemerides is summarized, and the SF method is extended to the global BeiDou navigation satellite system (BDS3) and Galileo navigation satellite system. Further test results show that the performance of MHGM and SF are comparable from the perspective of root mean squares (RMS) and the power spectrum analysis of double-differenced residuals, as well as the static positioning results. This implies that the space domain MHGM can obtain similar correction effects as the SF method in the observation domain, but the former is more flexible for modeling with various systems’ data. In addition, the established MHGM using the data of multi orbit periods demonstrates a better performance compared with that of only one orbit period, and an average improvement of 13.1% in the RMS of the double-differenced residuals can be achieved.

The multipath effect on carrier-phase observations is related to the observational environment of the station, and cannot be eliminated or attenuated by the differential method. In the actual dam environment of deformation monitoring, multipath is very complicated. It is the main error source of BDS-2 for high precision data processing. In this paper, a new method called the multi-point hemispherical grid model (MHGM) is studied and evaluated. This method uses the hemispheric grid model to attenuate the influence of multipath errors. The effectiveness and improvement of the new method with respect to the popular sidereal filter (SF) method were assessed and verified by the actual dam monitoring data with only the BDS-2. The MHGM and SF approach calculates the multipath corrections from multi-days historical data, and then corrects the multipath error from the observations on the day of interest. Compared with the SF method, the MHGM can also effectively provide a graphical display of multipath error interference around the stations, which matches the surrounding observation environment. The results show that the double-differenced (DD) residuals of the MHGM for GEO/IGSO and MEO satellites are slightly better than those of SF, but the RMS of MHGM for GEO/IGSO+MEO DD residuals is improved by about 17.0%. The performance of the MHGM method for different satellite constellations is consistent and not affected by the different orbital repeat times, which could solve the “zero mean” assumption problem of the SF method. Meanwhile, the RMS of static with a 2 h interval and kinematic positioning errors of the MHGM method in the horizontal and vertical direction can be up to 1.7, 3.6 and 2.4, 8.1 mm, respectively. The MHGM model could avoid the multipath reference problem caused by different orbital repetition periods, making it more suitable for the combined multipath modeling of BDS-2 and BDS-3.

The European Global Navigation Satellite System Galileo is gradually deploying its constellation. In order to provide reliable navigation and position services, the effectiveness and reliability of ambiguity resolution between reference stations is necessary in network real-time kinematic (NRTK). The multifrequency signal of Galileo could much enhance the ambiguity resolution (AR) reliability and robustness. In this study, to exploit full advantage of this, the geometry-free (GF) TCAR and ionospheric-free (IF) triple-carrier ambiguity resolution (TCAR) methods were utilized in solving the ambiguity in the Hong Kong area, which is an ionosphere disturbance active area, and compared with each other. The IF TCAR method was then used to combine multi-systems to improve Galileo E1 AR performance, which is named as the combined IF (CIF) TCAR method. Three experiments were carried out in the Hong Kong area and the results showed that the Galileo-only system could fix ambiguities on all satellite pairs correctly and reliably by the IF TCAR method, while the GF TCAR method showed a weaker performance. The wide-lane (WL) convergence time of the IF TCAR method is improved by about 37.6%. The IF TCAR method with respect to the GF TCAR method could improve the WL accuracy by 21.6% and the E1 accuracy by 72.7%, respectively. Compared with GPS-only TCAR or Galileo-only TCAR, the ambiguity accuracy and the convergence time of the CIF TCAR method, which combines GPS and Galileo, could be improved by about 25.7% and 47.1%, respectively.

The multipath effect is a crucial error source caused by the environment around the station and cannot be eliminated or mitigated by differential algorithms. Theoretically, the maximum value for the carrier phase is a quarter the wavelength, i.e., about 4.8 cm for the GPS L1 signal. Considering the increasing demands of high-precision applications, the multipath error has become a major factor affecting the accuracy and reliability of GPS millimeter-level data processing. This paper proposes a multi-point hemispherical grid model (MHGM) to mitigate the multipath effect. In this method, the hemisphere centered on each station is divided into a grid, and the multipath error at the station is estimated based on the parameterization of the grid points. The double-differenced (DD) observed-minus-calculated (OMC) values on some previous days are treated as the observation values to model the present multipath error. Contrary to the present methods which rely much on the platform of data collection and processing, MHGM can be potentially applied to GPS data processing with the existing hardware and software. Experiments in high-multipath and low-multipath environments are designed by mounting a baffle or not. The experimental results show that MHGM is effective in mitigating the multipath effect. When using data from the previous day, an average improvement of about 63.3% in the RMS of DD OMC can be made compared with that without correction, and this is basically consistent with the sidereal filtering (SF) method which is 63.0%. Furthermore, the effectiveness of the above two methods is better than that of the empirical site model (ESM). The kinematic positioning results are also basically consistent with the statistical results of the RMS values of DD OMC. Historical data from more than one day can more explicitly and effectively model the MHGM. Furthermore, compared with the SF method, the MHGM can be used not only to mitigate the multipath error, but also to orientate the sources of the multipath error around the station, and give guidance in the physical elimination of these sources.