Algorithm developed in this work is used to predict the location and estimate the traveling speed of a barge moving in inland waterway network. Measurements obtained from GPS or other systems are corrupted with measurement noise and reported at large, irregular time intervals. Thus, creating uncertainty about the current location of the barge and minimizing the effectiveness of emergency response activities in case of an accident or act of terrorism. Developing a prediction algorithm become a non-trivial problem due to estimation of speed becomes challenging, attributed to the complex interactions between multiple systems associated in the process. This software, uses systems approach in modeling the motion dynamics of the barge and estimates the location and speed of the barge at next, user defined, time interval. In this work, first, to estimate the speed a non-linear, stochastic modeling technique was developed that take local variations and interactions existing in the system. Output speed is then used as an observation in a statistically optimal filtering technique, Kalman filter, formulated in state-space to minimize numerous errors observed in the system. The combined system synergistically fuses the local information available with measurements obtained to predict the location and speed of traveling of the barge accurately.
To order this software or receive further information, please fill out the following request: Request Software
@misc{osti_1231499,
title = {TRACC: Algorithm for Predicting and Tracking Barges on Inland Waterways, Version 00},
author = {Randeniya, Duminda I.B.},
abstractNote = {Algorithm developed in this work is used to predict the location and estimate the traveling speed of a barge moving in inland waterway network. Measurements obtained from GPS or other systems are corrupted with measurement noise and reported at large, irregular time intervals. Thus, creating uncertainty about the current location of the barge and minimizing the effectiveness of emergency response activities in case of an accident or act of terrorism. Developing a prediction algorithm become a non-trivial problem due to estimation of speed becomes challenging, attributed to the complex interactions between multiple systems associated in the process. This software, uses systems approach in modeling the motion dynamics of the barge and estimates the location and speed of the barge at next, user defined, time interval. In this work, first, to estimate the speed a non-linear, stochastic modeling technique was developed that take local variations and interactions existing in the system. Output speed is then used as an observation in a statistically optimal filtering technique, Kalman filter, formulated in state-space to minimize numerous errors observed in the system. The combined system synergistically fuses the local information available with measurements obtained to predict the location and speed of traveling of the barge accurately.},
doi = {},
url = {https://www.osti.gov/biblio/1231499},
year = {Fri Apr 23 00:00:00 EDT 2010},
month = {Fri Apr 23 00:00:00 EDT 2010},
note =
}
