Random MHT data association algorithm based on random coefficient Kalman filter
Yi Zhang, Xiaojing Shen, Zhiguo Wang, Yunmin Zhu · 2017
A novel random data association algorithm is proposed in the framework of multiple hypothesis tracking which can be equivalent to an NP-hard multidimensional assignment problem. The key idea of this new algorithm is to relax the multidimensional assignment problem to a linear programming problem. The solution of the linear programming problem may be treated as the probability of potential tracks, which avoids the computation difficulties of rounding the probability solution to 0/1 satisfying the constraints. Then all tracks and measurements can be integrated to a new whole dynamic system with random coefficient matrices. Moreover, the random coefficient matrix Kalman filtering is applied to the integrated dynamic system to derive the state estimates of the tracks. The significant advantage of the random Kalman filter-based multiple hypothesis tracking data association is that the computation complexity is much less than that of Lagrangian relaxation of the multidimensional assignment problem. Simulation demonstrates the random data association algorithm works well and the running time can be shortened greatly compared with the Lagrangian relaxation algorithm of the multidimensional assignment problem.