A convex minimization approach to data association with prior constraints

In this paper we propose a new formulation for reliably solving the measurement-to-track association problem with a priori constraints. Those constraints are incorporated into the scalar objective function in a general formula. This is a key step in most target tracking problems when one has to handle the measurement origin uncertainty. Our methodology is able to formulate the measurement-to-track correspondence problem with most of the commonly used assumptions and considers target feature measurements and possibly unresolved measurements as well. The resulting constrained optimization problem deals with the whole combinatorial space of possible feature selections and measurement-to-track correspondences. To find the global optimal solution, we build a convex objective function and relax the integer constraint. The special structure of this extended problem assures its equivalence to the original one, but it can be solved optimally by efficient algorithms to avoid the cominatorial search. This approach works for any cost function with continuous second derivatives. We use a track formation example and a multisensor tracking scenario to illustrate the effectiveness of the convex programming approach.