As one of the main methods in positioning and navigation, the Global Navigation Satellite System (GNSS) is widely applied in many areas, such as road engineering, automobile navigation and many other outdoor applications. In addition to an outdoor location-based service, there are many needs for indoor positioning. However, GNSS is hard to position indoors due to signal blockage, and outdoor–indoor transition areas where multipath effects are unavoidable and signal interference is severe. This article proposes a GNSS/INS/UWB tightly-coupled integration positioning methodology to address the problem of accurate positioning in GNSS-difficult areas and aims to provide seamless positioning in different scenarios. In indoor environments, the system uses the UWB/INS tightly-coupled integration positioning mode and periodically corrects the INS errors with range measurements obtained from the UWB transmitting unit to achieve centimeter-level accuracy. In outdoor GNSS-difficult areas like outdoor–indoor transition areas, a GNSS/INS/UWB tightly-coupled integration positioning system is proposed to ensure positioning accuracy continuity and further improve the reliability of the system. To evaluate the positioning performance of the proposed UWB enhanced multi-sensor tightly-coupled integration system, an experiment was conducted in a high-rise building. The experiment results show that the proposed integrated positioning system can provide seamless and accurate positioning results in both indoor and outdoor–indoor transition areas, with DRMS of 5.25 cm and RMSE of 10.18 cm.

This paper describes an adaptive fault-tolerant triple-integration vehicle navigation system. The proposed triple-integration system is based on a tightly coupled design, which fuses measurements from the Global Navigation Satellite System (GNSS), an Inertial Navigation System (INS), and optical velocity sensors (OVS). The difference between GNSS-measured and INS-derived pseudorange and pseudorange rate, and the difference between INS-derived and OVS-measured velocity are taken as the measurement inputs to the system filter, which uses them to obtain accurate and continuous navigation solutions. However, to ensure the optimality and to improve the robustness of the integration system in GNSS-challenging environments, a fault-tolerant fault detection and processing (FDP) algorithm is then applied to detect faults and further process the detected faults in fault-varying methods. The detected faults can be classified as Outliers and Biases according to the designed fault factor; Outliers are excluded and Biases are still utilized by adaptively adjusting the corresponding measurement noise covariance matrix. To evaluate the performance of the proposed triple-integration system, a road test was conducted under different scenarios, including GNSS open-sky, GNSS partly blocked, and GNSS-difficult conditions. The results indicate that the proposed integration system can generate more accurate position solutions than a loosely coupled system. To verify the fault-tolerant ability of the proposed FDP algorithm, typical types of GNSS faults are simulated. The results confirm that the proposed FDP algorithm can improve the system performance in terms of its fault-tolerant ability and accuracy as well.

The BeiDou Navigation Satellite System (BDS) has been widely applied in many areas, including communications, transportation, emergency rescue, public security, surveying, precise positioning, and many other industrial applications. The integration of BDS and an Inertial Navigation System (INS) has the ability to achieve a more consistent and accurate positioning solution. However, during long BDS outages, the performance of such an integrated system degrades because of the characteristics of the Inertial Measurement Unit (IMU) - the sensor errors accumulate rapidly with time when operated in a standalone mode. In this paper, a BDS/INS/odometer/map-matching (MM) positioning methodology for train navigation applications is proposed, to solve the problem of positioning during BDS outages when trains pass through signal obstructed areas such as under bridges, inside tunnels and through deep valleys. The seamless transition of the train operation in various scenarios can be therefore maintained. When the train operates in an open-sky environment the BDS signals are available to provide accurate positioning, and the integrated BDS/INS/odometer/MM system is used to correct the INS errors using BDS measurements and to calculate the velocity using the odometer in the navigation frame so as to improve the system reliability and accuracy. In addition, the integrated system also delivers accurate positioning measurements at a high update rate. When the BDS signals are blocked the integrated system switches to the INS/odometer/MM mode, and the integrated system corrects the INS errors by using odometer measurements. In order to evaluate the proposed system a real experiment was conducted on the Qinghai-Tibet railway in western China. The experimental results indicate that the proposed system can provide accurate and continuous positioning results in both open-sky and BDS signal-obstructed environments.

GNSS/INS integrated navigation and positioning technology is widely used for train operations applications seeking a decrease in dependence on trackside equipment. However, during train operations, environmental factors such as bridges, tunnels, hillsides, and buildings will block the satellite signals. When INS works in a stand-alone mode, the system errors accumulate with time due to the inertial sensor characteristics. To overcome this problem, light detection and ranging (lidar) equipment can be introduced to help control INS divergence. The lidar point cloud data is first processed using the random sample consensus (RANSAC) algorithm to determine the track plane, then the moving average filtering (MAF) algorithm is used to detect and classify the rails. The frogs can be detected based on the changing rail number and the constraint condition for turnout parameters. With the assistance of a previously surveyed turnout database, the geographical positions of the detected frogs can be obtained and integrated with INS data to converge INS sensor errors when GNSS is not available. In this paper a lidar-aided GNSS/INS integrated navigation system is proposed that can ensure continuous positioning in both GNSS-available and GNSS-blocked scenarios. GNSS/INS is applied when the system operates in the GNSS-available scenario, and switches to the INS/lidar mode when the GNSS signals are blocked. To evaluate the performance of the proposed system, an actual train experiment was conducted along the Qinghai-Tibet railway. The results confirm that the proposed lidar-aided navigation system can achieve accurate and continuous positioning. When GNSS is not available, the proposed method can effectively suppress the error of INS with a DRMS of 1.66 m, which is a 70.4% improvement compared with a conventional GNSS/INS integrated system.

GNSS/INS integration is widely used for train positioning, but railways tunnels and mountains can interfere GNSS signals and will lead to performance degradation when the system is operated in the standalone INS mode. This paper proposes a Long Short Term Memory (LSTM)-assisted GNSS/INS integration system using recomputed Inertial Measurement Unit (IMU) error, to suppress the error divergence of an INS in the case of GNSS solution non-availability. The IMU error recomputation method is firstly proposed, where the GNSS/INS-derived position, velocity and attitude (PVA) information is utilized when is GNSS available. The train's attitude computed using the GNSS dual-antenna moving baseline method is used as the heading constraint for GNSS/INS integration so as to provide accurate attitude information. The recomputed IMU sensor error is then used for model training, and the system switches to LSTM-assisted INS mode when GNSS solutions are unavailable. The system predicts the IMU sensor error using the train motion state, and corrects the IMU measurements to suppress the accumulating IMU sensor error.The proposed system was evaluated through a train experiment on the Shuozhou-Huanghua railway. The IMU error Recomputation Method (IMU-RM) was evaluated on four time slots of varying lengths, and the proposed LSTM-assisted GNSS/INS integration system using IMU-RM was evaluated in two “difficult” GNSS signal areas, of curved and straight railtrack segments, were simulated. Results showed significant improvement in horizontal position accuracy compared to conventional methods, with suppression of INS sensor error divergence by 79% and 63% for curved and straight segments, respectively.

We describe an integrated navigation system based on Global Navigation Satellite Systems (GNSS), an Inertial Navigation System (INS) and terrestrial ranging technologies that can support accurate and seamless indoor-outdoor positioning. To overcome severe multipath disturbance in indoor environments, Locata technology is used in this navigation system. Such a “Locata-augmented” navigation system can operate in different positioning modes in both indoor and outdoor environments. In environments where GNSS is unavailable, e.g. indoors, the proposed system is designed to operate in the Locata/INS “loosely-integrated” mode. On the other hand, in outdoor environments, all GNSS, Locata and INS measurements are available, and all useful information can be fused via a decentralised Federated Kalman filter. To evaluate the proposed system for seamless indoor-outdoor positioning, an indoor-outdoor test was conducted at a metal-clad warehouse. The test results confirmed that the proposed navigation system can provide continuous and reliable position and attitude solutions, with the positioning accuracy being better than five centimetres.