Ant Colony Optimization Heuristic for the Multidimensional Assignment Problem in Target Tracking

Associating measurements with targets is an important step in target tracking. With the increasing computational power, it became possible to use more complex association logic in tracking algorithms. Although it's optimal solution can be proved to be an NP hard problem, the multidimensional assignment enjoyed a renewed interest mostly due to Lagrangian relaxation approaches to its solution. Recently, it has been reported that randomized heuristic approaches surpassed the performance of Lagrangian relaxation algorithm especially in dense problems. In this paper, inspired by the success of randomized heuristic method, we investigate a different stochastic approach, the biologically inspired ant colony optimization to solve the NP hard multidimensional assignment problem.