A novel estimator with moving ants

In the traditional ant colony optimization (ACO) algorithm, ants are utilized to solve various combinatorial optimization problems regardless of their individual velocities. In this work, however, a moving ant estimator (MAE) is developed on the premise that the issue of moving velocity of each ant is considered. The determination of the velocity of each ant depends directly on the normalized weights between the one-step prediction of the ant starting position and the selected one-step position. Besides this, the velocity of each ant is further adjusted locally based on its individual moving ability, which is defined in the corresponding “pheromone” update process just as the traditional ACO algorithm. To improve the accuracy of the proposed estimator, two improved versions are investigated. Numerical simulations show that the moving ant estimator, as well as its two improved versions, could estimate adaptively the state of maneuvering or non-maneuvering target. To verify the effectiveness of the MAE, the obtained results are compared with those from PF, IMMPF, etc.