A neural network for data association

Abstract

This paper presents a new neural solution for solving the data association problem. This problem, also known as the multidimensional assignment problem, arises in data fusion systems like radar and sonar targets tracking, robotic vision... Since it leads to an NP-complete combinatorial optimization, the optimal solution can not be reached in an acceptable calculation time, and the use of approximation methods like the Lagrangian relaxation is necessary. In this paper, we propose an alternative approach based on a Hopfield neural model. We show that it converges to an interesting solution that respects the constraints of the association problem. Some simulation results are presented to illustrate the behaviour of the proposed neural solution for an artificial association problem.