• Journal of the Korea Institute of Information and Communication Engineering
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• v.10 no.11
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• pp.2002-2007
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• 2006

The path search method in the telematics system should consider traffic flow of the roads as well as the shortest time because the optimal path with minimized travel time could be continuously changed by the traffic flow. The existing path search methods are not able to cope efficiently with the change of the traffic flow. The search method to use traffic information also needs more computation time than the existing shortest path search. In this paper, a method for efficiency improvement of path search is implemented and its performance is evaluated. The method employs the fixed grid for adjustable heuristic to traffic flow. Moreover, in order to simplify the computation of estimation values, it only adds graded decimal values instead of multiplication operation of floating point numbers with due regard to the gradient between a departure and a destination. The results obtained from the experiments show that it achieves the high accuracy and short execution time as well.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39C no.1
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• pp.17-27
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• 2014

As the importance of Cooperative Engagement Capability and network-centric warfare has been dramatically increasing, it is necessary to develop distributed tracking systems. Under the development of distributed tracking systems, it requires tracking filters and data fusion theory for nonlinear systems. Therefore, in this paper, the problem of nonlinear track fusion, which is suitable for distributed networks, is formulated, four algorithms to solve the problem of nonlinear track fusion are introduced, and performance of introduced algorithms are analyzed. It is a main problem of nonlinear track fusion that cross-covarinaces among multiple platforms are unknown. Thus, in order to solve the problem, two techniques are introduced; a simplification technique and a approximation technique. The simplification technique that help to ignore cross-covariances includes two algorithms, i.e. the sample mean algorithm and the Millman formula algorithm, and the approximation technique to obtain approximated cross-covariances utilizes two approaches, by using analytical linearization and statistical linearization based on the sigma point approach. In simulations, BCS fusion is the most efficient scheme because it reduces RMSE by approximating cross-covariances with low complexity.