The integration of Global Positioning System (GPS) real-time kinematics (RTK) and an inertial navigation system (INS) has been widely used in many applications, such as mobile mapping and autonomous vehicle control. Such applications require high-accuracy position information. However, continuous and reliable high-accuracy positioning is still challenging for GPS/INS integration in urban environments because of the limited satellite visibility, increasing multipath, and frequent signal blockages. Recently, with the rapid deployment of multi-constellation Global Navigation Satellite System (multi-GNSS) and the great advances in low-cost micro-electro-mechanical-system (MEMS) inertial measurement units (IMUs), it is expected that the positioning performance could be improved significantly. In this contribution, the tightly-coupled single-frequency multi-GNSS RTK/MEMS-IMU integration is developed to provide precise and continuous positioning solutions in urban environments. The innovation-based outlier-resistant ambiguity resolution (AR) and Kalman filtering strategy are proposed specifically for the integrated system to resist the measurement outliers or poor-quality observations. A field vehicular experiment was conducted in Wuhan City to evaluate the performance of the proposed algorithm. Results indicate that it is feasible for the proposed algorithm to obtain high-accuracy positioning solutions in the presence of measurement outliers. Moreover, the tightly-coupled single-frequency multi-GNSS RTK/MEMS-IMU integration even outperforms the dual-frequency multi-GNSS RTK in terms of AR and positioning performance for short baselines in urban environments.

Nonholonomic constraint (NHC) and odometer speed have been proven to significantly improve the navigation accuracy of a global navigation satellite system (GNSS)-aided inertial navigation system (INS) for land vehicular applications. Exploiting the full potential of the NHC and odometer aids requires the inertial measurement unit (IMU) mounting angles, i.e., angular misalignment with respect to the host vehicle, to be precisely known. We address the accurate estimation of the IMU mounting angles through an aided dead reckoning (DR) approach. In this method, DR using the GNSS/INS integrated attitude and distance traveled is fused with the GNSS/INS integrated position through a straightforward Kalman filter. Simulation and field tests are carried out to validate the proposed algorithm for different grade IMUs, including typical navigation-grade, tactical-grade and low-cost IMUs. The results demonstrate that the pitch and heading mounting angles can be estimated with a comparable accuracy with the GNSS/INS attitude solution, for example, 0.001° accuracy can be achieved for a navigation-grade GNSS/INS integrated system. The roll mounting angle can not be estimated due to lack of observability in this approach, and the heading mounting angle estimation may be influenced by the GNSS/INS heading accuracy drift to some extent for the low-cost IMUs.

Accurate measurement of the railway track geometry is a task of fundamental importance to ensure the track quality in both the construction phase and the regular maintenance stage. Conventional track geometry measuring trolleys (TGMTs) in combination with classical geodetic surveying apparatus such as total stations alone cannot meet the requirements of measurement accuracy and surveying efficiency at the same time. Accurate and fast track geometry surveying applications call for an innovative surveying method that can measure all or most of the track geometric parameters in short time without interrupting the railway traffic. We provide a novel solution to this problem by integrating an inertial navigation system (INS) with a geodetic surveying apparatus, and design a modular TGMT system based on aided INS, which can be configured according to different surveying tasks including precise adjustment of slab track, providing tamping measurements, measuring track deformation and irregularities, and determination of the track axis. TGMT based on aided INS can operate in mobile surveying mode to significantly improve the surveying efficiency. Key points in the design of the TGMT’s architecture and the data processing concept and workflow are introduced in details, which should benefit subsequent research and provide a reference for the implementation of this kind of TGMT. The surveying performance of proposed TGMT with different configurations is assessed in the track geometry surveying experiments and actual projects.

We investigate the long-term performance and improvement of high-rate multiconstellation Global Navigation Satellite System (GNSS) or multi-GNSS attitude determination in a dynamical environment and evaluate how much each GNSS contributes to such an improvement with applications to land vehicle navigation. We build a multiantenna GNSS system with three nondedicated receivers and conduct two experiments with quad-constellations in GNSS-friendly and GNSS-challenged environments on January 19 and 23, 2018, respectively. A high-grade inertial measurement unit (IMU) provides a reference of comparison for dynamic attitude solutions. In the static case, the global positioning system (GPS), BeiDou navigation satellite system (BDS), and GLObal NAvigation Satellite System (GLONASS) single-constellation solutions have basically the same performance for both experiments. In the dynamic case, GPS achieves the best accuracy in the experiment on January 19, while on January 23, GPS and GLONASS achieve very similar accuracy and significantly outperform BDS in the pitch and roll components. The different dynamic performances of these single constellations are primarily attributed to their differences in observation noise level and satellite geometry strength. By combining GPS, BDS, GLONASS, and Galileo, the multi-GNSS solution performs significantly better than any single-constellation solution. The average improvements in the dynamic cases are 26.2%, 40.7%, and 36.0% on January 19 and 19.2%, 30.0%, and 36.2% on January 23 in the yaw, pitch, and roll components, respectively. The attitude solutions on January 19 are always better determined than on January 23 in the dynamic cases, which should clearly indicate the importance of combining multiple GNSS constellations and the benefit of more observed satellites and a stronger satellite geometry in GNSS-challenged environments. We also introduce the concept of “influence rate” and quantitatively analyze the contribution of each GNSS system to the combined attitude solution.

Using a microelectromechanical system (MEMS) inertial measurement unit (IMU) array composed of multiple low-cost IMUs can reduce the measurement errors of inertial sensors, and improve its navigation performance. However, there is doubt about the benefit of the IMU array in the scene of GNSS/INS dynamic navigation . Therefore, to evaluate the navigation performance of the arrays, we developed four groups of IMU arrays, each containing 16 MEMS IMUs. Each IMU was accurately calibrated and compensated to improve the performance. The field experiments proved that the navigation accuracy of the IMU array improved by 3.4 times statistically over a single IMU, which is close to the theoretical limit, i.e., 4 times. Comparison data processing indicated that the individual IMU calibration reduced the horizontal position error of the array by 54% on average, which confirms that the precise calibration of each IMU, especially the cross-axis effect and mounting angles, is crucial to the array’s navigation performance. This research provides firm experimental support for the application of IMU arrays in the field of navigation.

Magnetic field matching (MFM) positioning is one of the mainstream methods of consumer indoor positioning, which has attracted great attention from academia and industry. However, efficient methods for constructing magnetic field maps and matching positioning methods with high stability still need to be developed. This study proposes an indoor MFM positioning scheme enhanced by consumer-grade inertial measurement units that can efficiently generate a magnetic field grid map and achieve robust matching positioning, without the need to actively calibrate the magnetometer bias. When generating the magnetic field map, the proposed method employs a pedestrian positioning and orientation system to efficiently collect data by releasing individual behavioral constraints and reducing the number of control points required. Moreover, a magnetometer bias auto-calibration method, aided by the precise attitude, is proposed to simplify the data collection process. During real-time positioning, the position and attitude generated by pedestrian dead reckoning (PDR) are used to generate the differential magnetic field strength in the sensor frame; this achieves matching positioning that is independent of the magnetometer bias. Further, an estimation method for the MFM position noise, based on the magnetic field gradient, is proposed to improve the accuracy of MFM/PDR integrated positioning. Several experiments were conducted to verify the feasibility and performance of the proposed scheme. Only slight differences were found between the magnetic field maps using three different smartphones, showing that the proposed scheme can efficiently generate a high-precision magnetic map. The positioning results of multiple tests conducted using eight different smartphones revealed that the proposed scheme achieves continuous and robust meter-level positioning accuracy.