Search

The integration of candidate sensors such as a microelectromechanical system-based inertial measurement unit (MEMS IMU) for localization and navigation has been widely explored for vehicular, pedestrian, and robotics applications. They also arise as candidate sensors in the train navigation context, and their integration in safety-relevant applications has become a prominent area of interest. This requires sensor modeling in a specific context, the search of new information sources (e.g., digital maps), the development of navigation algorithms, and the testing of the system. In this context, this article proposes a one-stop simulation framework for testing train navigation algorithms that uses nine degrees of freedom (DOFs) IMU and tachometers. The simulation framework can generate synthetic signals based on the dynamic model of a train, digital maps, speed profiles, and a sensor error model. It also uses real signals recorded from experimental measuring campaigns. Train navigation algorithms are then executed. Finally, the performance of the obtained results is evaluated. The main contribution made by this framework is its flexibility when used to evaluate different sensors, train configurations, tracks, mechanization methods, navigation algorithms, and so on during different stages of the development process. Simulation framework use cases are presented in this article, where several mechanization methods and train navigation algorithms are tested. Finally, the experimental data are recorded and included in the presented simulation framework to evaluate an example of a train navigation algorithm based on a 9-DOF-IMU and tachometers. The mean differences in the synthetic and experimental IMU signals are around 3.52% in accelerometers, below 0.01% in gyroscopes, and 10.16% in magnetometers in the acceleration sections (i.e., when the train is applying torque). [ABSTRACT FROM AUTHOR]

Integrating new candidate sensors, such as Global Navigation Satellite System (GNSS) and inertial measurement unit (IMU), into fail-safe train positioning systems have recently become a prominent area of research. Although there are a number of contributions related to the design of data fusion algorithms, the lack of details in raw measurements analysis has directly motivated this paper. This paper aims to record data from a variety of sensors (such as GNSS, IMU, magnetometer, barometer, tachometers, and Doppler radars) to evaluate train velocity and railway features (track slope, curve cant, and radius) extending previous works in the instrumentation and measurement field. The field test designed and concisely described in this paper presents several challenging environments, such as a tunnel, which can be used to analyze the candidate sensors limitations. In addition, a demonstration of a data fusion algorithm is presented to calculate train velocity based on measurements from the candidate sensors. The results obtained by an extended Kalman filter using GNSS and IMU are compared with velocity recorded by tachometers and Doppler radars, which is considered to be the reference value. The calculated velocity by IMU and GNSS when both sensors measurements are available presents an absolute error in velocity lower than 2 km/h in more than 90% of test duration. Finally, railway features (curve radius, cant, and slope) are calculated and analyzed according to train and railway dynamics. [ABSTRACT FROM AUTHOR]

Positioning accurately and safely a train is nowadays a great challenge. That includes currently available railway sensors and new candidate sensors for data fusion. Global Navigation Satellite System and Inertial Measurement Unit sensors arise as prominent technologies to incorporate in railways. Although satellite-based train localization tests can be found in the scientific literature, there are no common criteria to evaluate the performance of the positioning achieved. In this paper, a series of criteria is defined and justified in order to be able to evaluate the most recent and relevant works related to train positioning. The results of this comparative analysis are gathered in tables, where the criteria defined are applied to the works compiled. According to the results obtained, a research gap in safety related applications is found. It is concluded that the economic viability of given solutions should be explored, so as to design an on-board train-integrated positioning system. [ABSTRACT FROM PUBLISHER]