A Fast and Efficient Data Association of Passive Sensor Tracking

Data association is one of the key and difficult problems for multisensor-multitarget tracking. The classic multidimensional assignment algorithm often uses Lagrange relaxation algorithm to solve association problem with the angle only data obtained by passive sensors in presence of clutter, false alarm condition. The sub gradient is applied to update the Lagrange multipliers, but it needs to minimize all the sub problems at every iterative time to solve the dual solution in the classic algorithm. This leads to long compute time and bad real-time performance. Aimed at the problem, an improved data association algorithm based on the Lagrange relaxation algorithm is introduced in this paper. It uses the surrogate modified sub gradient to update the Lagrange multipliers. Compared with the classical algorithm, new algorithm has less compute time and higher association accuracy via simulation.