Open Access Open Access  Restricted Access Subscription Access

Multiple Target Tracking Using Ant Colony Optimization and Artificial Neural Network

Endra Joelianto

Abstract


The main problem in multi-sensor-multi-target tracking is data association problem that leads to multidimensional assignment problems. In this paper, multidimensional assignment problem is solved using ant colony optimization algorithm to obtain the number of targets. Then the state of the targets are estimated using Kalman filter, and estimation improved using neural network. In ant colony optimization algorithm, has developed the process of making initial pheromone value of ants and ants placement in the second cycle and furthermore. It is found that ant colony optimization algorithm will get better search results and faster if the initial pheromone value is taken equal to the
visibility and placed new ants initial position at the second cycle and furthermore on the point of the second visit of the previous cycle of ants.

Full Text:

PDF

References


Kammerdiner A.R., P.A. Krokhmal, P.M. Pardalos, Characteristics of the Distribution of Hamming Distance Values Between Multidimensional Assignment Problem Solutions, LNCIS 369, pp. 339-352, Spinger-Verlag, Berlin Heidelberg, 2007.

Chen G., L. Hong, A Genetic Algorithm based Multidimensional Data Association Algorithm for Multi-sensor Multi-target Tracking, Pergamon, Mathl. Comput. Modelling Vol. 26, No. 4: 57-69, 1997

Turkmen, I., K. Guney, Genetic tracker with adaptive neuro-fuzzy inference system for multiple target tracking, ScienceDirect, Expert System with Applications ,35, 1657-1667, 2008.

Turkmen, I., K. Guney, Tabu searh tracker with adaptive neuro-fuzzy inference system for multiple target tracking, Progress In Electromagnetics Research, PIER ,65, 169-185, 2006.

Bonabeau, E., M. Dorigo, G. Theraulaz, Swarm Intelligence: from natural to artificial systems, New York, Oxford University Press, 1999.

Grewal, M.S., A.P. Andrews, Kalman Filtering,Theory and Practice Using MATLAB, New Jersey, John Wiley & Sons Inc., 2008.

Krose B., P. van der Smagt, An Introduction to Neural Networks,The University of Amsterdam , 1996.

Stimson, G.W., Introduction to Airborne Radar,2nd Ed., New York, Scitech Publishing Inc., 1998.

Popp R.L., K.R. Pattipati, Y. Bar-Shalom, m-Best S-D Assignment Algorithm with Application to Multitarget Tracking, IEEE Transactions on Aerospace and Electronic Systems Vol. 37, No. 1: 22-39, 2001.

Vaidehi, V., N. Chitra, M. Chokkalingam, C.N. Krishnan, Neural network aided Kalman filtering for multitarget tracking applications, Pergamon, Computer and Electrical Engineering 27, pp. 217-228, 2001.




DOI: http://dx.doi.org/10.21535%2FProICIUS.2010.v6.522

Refbacks

  • There are currently no refbacks.