Global Optimal Track Association Algorithm Based on Sequential Modified Grey Association Degree

Track association is a precondition of the distributed multi-sensors track fusion. Given the fact that the fusion center is not able to get the target states estimation covariance, a global optimal track association algorithm based on sequential modified grey association degree is proposed. The algorithm cancels the scope normalization, sequentially accumulates data array index absolute difference and modifies the grey association coefficient formulation to ensure exchangeability, thus yielding the sequential modified grey association degree between the sensors tracks. Then the global optimal track association is obtained by making the association degree as the global statistical vector. The simulation results show that the performance and robustness of the proposed algorithm is apparently better than the traditional algorithm under the condition of dense parallel formation, random cross targets and unshared observation in existence.