Train localization during Global Navigation Satellite Systems (GNSS) outages presents challenges for ensuring failsafe and accurate positioning in railway networks. This paper proposes a minimalist approach exploiting track geometry and Inertial Measurement Unit (IMU) sensor data. By integrating a discrete track map as a Look-Up Table (LUT) into a Particle Filter (PF) based solution, accurate train positioning is achieved with only an IMU sensor and track map data. The approach is tested on an open railway positioning data set, showing that accurate positioning (absolute errors below 10 m) can be maintained during GNSS outages up to 30 s in the given data. We simulate outages on different track segments and show that accurate positioning is reached during track curves and curvy railway lines. The approach can be used as a redundant complement to established positioning solutions to increase the position estimate's reliability and robustness.

Determination of train positions within a railway network must be fail-safe and of high accuracy. In train-bourne positioning, exploitation of geometrical map features is an important factor and uncertainties in the map information may affect the position estimate. In this paper, we present a method to estimate the position of a train in the track net and to identify the correct path behind a turnout, using absolute position estimates and geometrical map information of various accuracies. We evaluate the impact of uncertainties in the map representation on the correct identification of a path behind a turnout. We derive a formulation of a probabilistic track map and include the map information into a constrained multi-hypothesis Kalman filter. We show in numerical simulations on a crossover and a turnout that modelling existing map uncertainties significantly improves the track discrimination.

Determination of train positions within a railway network must be fail-safe and of high accuracy. This is an essential task to solve to achieve a secure and efficient railway operation. In this paper, we present a method to estimate position and velocity of a train in the track net using given position estimates from an arbitrary information source, and improving the estimate by using geometrical track information. We focus on modelling and exploiting of the geometrical track information including possible uncertainties and examine the impact of uncertainties on the state estimate. We store the track information as a set of supporting points with Gaussian uncertainties and interpolate linearly. The track information is fed into a Kalman filter in form of soft constraints that is modified to account for state-dependent observation noise. A simulated test run shows that the average position and velocity error along track decreases significantly when modelling the uncertainty of the constraints, compared to using a Kalman filter with hard constraints. We evaluate the presented filter for different supporting point and measurement uncertainties and show that the performance within a typical parameter setting for train positioning is improved compared to the unconstrained Kalman filter and the Kalman filter with hard constraints.

In the previous study [1], a novel contrast pulse sequence, CPS4, was introduced. The CPS4 combined sub-harmonic, ultra-harmonic and pulse inverse imaging to provide an improved contrast-to-tissue ratio (CTR). The CPS4 emits two pairs of transmitting waves at frequencies of f0/2 and 2*f0 with inversed phase within each pair and filters the received echoes at the frequency of f0. However, the performance of CPS4 was not optimized. Simulation study [2] shows that there is a pressure threshold for the sub-harmonic response generation of the ultrasound contrast agent (UCA). The threshold is expected to reach its local minima with the transmitting frequency around the resonance frequency. By lowering the threshold, more MBs could be excited to response sub-harmonic signal which could improve the CTR of CPS4.

The current study aims to investigate frequency-dependent performance of CPS4 with the polyvinyl alcohol microbubbles (PVA MBs). First a linear oscillator model adapted from Hoff and Church[3, 4] was built for single PVA MB. The attenuation and phase velocity of a PVA MB suspension were obtained to calibrate the linear oscillator. The model was used to estimate the resonance frequency of the MBs. The transmitting frequency of CPS4 for sub-harmonic was set at four frequency points around the local minima, i.e. resonance frequency. The performance of CPS4 at different frequencies were evaluated.

Simulations of microbubbles (MBs) suggest that the excitation threshold for sub-harmonic generation is frequency-dependent. The minimum threshold, dependent on models and assumptions, might appear near the resonance frequency. Given that, in the current study, attempts were made to optimize a novel contrast pulse sequence, CPS4, for the in-house ultrasound contrast agent, polyvinyl alcohol microbubbles (PVA MBs) by setting the transmitting frequency near their resonance frequency to boost the sub-harmonic response. An improved model for PVA MBs was proposed to predict the resonance frequency. An in-vitro experiment was performed to evaluate the performance of CPS4 at different transmitting frequencies. The experiment results suggest the optimal performance of CPS4 with PVA MBs aqueous suspension appeared at the transmitting frequency of 11.25 MHz, which agrees with the values of the damped resonance frequency determined from simulation. The influence of the liquid environment on the performance of the CPS4 was also studied. The replacement of water with artificial blood degrades the contrast-to tissue ratio of CPS4 and shifts the optimal performance to the higher transmitting frequency.