access icon free Train distance and speed estimation using multi sensor data fusion

The accurate distance and speed estimates of train and individual coaches are necessary for the safe operation of the high-speed train system. Often, the train system does not rely on a single sensor for its distance and speed measurements as the sensors are susceptible to diverse operating conditions such as snow, rain, fog, tunnel, hilly region, slip, slide, etc. Hence, the information from a combination of sensors which can complement each other under certain operating conditions is required for the correct estimation. For distance measurements, Global Navigation Satellite System (GNSS) and balise are generally used. For speed sensing, the combination of wheel sensor, radar and GNSS are chosen. The diversity of sensors in terms of sampling rate and noise characteristics, etc. greatly affect the overall estimation accuracy and reliability if the measurements are used directly. Hence, this work presents a probabilistic weighted fusion algorithm which is based on the nonlinear longitudinal train dynamic model. The fusion algorithm combines the state estimates from distributed and sensor-specific extended Kalman filters. The effectiveness of the proposed fusion algorithm is demonstrated on the simulated sensor measurements along with a wide range of noises, spurious measurements, train operating conditions and track environmental disturbances.

Inspec keywords: reliability; state estimation; velocity measurement; nonlinear filters; distance measurement; Kalman filters; sensor fusion; probability; satellite navigation

Other keywords: GNSS; speed sensing; train distance estimation; Global Navigation Satellite System; multisensor data fusion; state estimation; wheel sensor; continuous spot train distance measurements; tunnel; high-speed train system; sensor-specific distributed extended Kalman filters; reliability; speed measurements; probabilistic weighted fusion algorithm; speed estimation algorithm; noise characteristics

Subjects: Spatial variables measurement; Velocity, acceleration and rotation measurement; Spatial variables measurement; Probability theory, stochastic processes, and statistics; Radionavigation and direction finding; Other topics in statistics; Velocity, acceleration and rotation measurement; Reliability

http://iet.metastore.ingenta.com/content/journals/10.1049/iet-rsn.2018.5359
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