Gray track-to-track correlation algorithm for distributed multitarget tracking system

Track correlation is a fundamental problem in distributed multitarget multisensor tracking system. It entails selecting the most probable association between target tracks from a very large set of possibilities. First, the inherent disadvantages of the present two kinds of track correlation algorithms are pointed out in this paper. Then a novel track correlation algorithm based on the gray theory is presented and the detailed steps are described. This method can weigh with the degree of relative correlation through the developing trend between the factors in data arrays, which breaks through the limitations of the sample quantities and the typical distributing rule. Effect of discrimination coefficient is also discussed. At last, the correct correlation rate and computational time cost of the proposed algorithm are compared with weighted algorithm and statistical double threshold algorithm through simulation. The simulation results show that the performance of gray track correlation algorithm proposed in this paper is much better than that of the classical methods under the environments of dense targets, interfering, noise, tracks cross and so on.