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.
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