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In this paper, we present a method based on belief functions to evaluate the quality of the optimal assignment solution of a classical association problem encountered in multiple target tracking applications. The purpose of this work is not to provide a new algorithm for solving the assignment problem, but a solution to estimate the quality of the individual associations (pairings) given in the optimal assignment solution. To the knowledge of authors, this problem has not been addressed so far in the literature and its solution may have practical aspects for improving the performances of multisensor-multitarget tracking systems.

- (e.g., local IMS, regional IMS or national surveillance center). This paper will discuss IMS, propose an architecture for an IMS system and give some specifications for the proposed IMS system. The paper will describe the implemented data fusion engine. Desirable features are also given in the paper.

Most visual servoing approaches explore the requirements for tracking and eventually grasping of a moving object based on the task at hand. On the other hand, active vision relies on the properties of the environment and the task to define a strategy of measurements by one or more cameras. The focus of our work is to achieve a high level of interaction between the two approaches. In fact the proposed approach addresses at the same time the optimization of the observation process and the achievement of the task at hand. Simulation results are presented for the case of an uncertain pendulum movement. An integrated sensing and actuation system that can operate in a dynamic as well as static environment is presented. It is a multi-sensor system that coordinates observations control, robotic arm command and intercepting the moving object.