To address the track-to-track association problem in the presence of time-varied sensor biases and different targets reported by different sensors, Gaussian Mixture Model (GMM) and neighborhood topology information are used in this paper. The robust track-to-track association problem is turned into a non-rigid point matching problem. The Gaussian mixture model is established with better robustness to ‘unpaired’ tracks. The weight of each Gaussian component is decided by the neighborhood topology information between tracks. The optimal closed solution of the Gaussian mixture model is solved by Expectation Maximization (EM) algorithm. In Expectation-step of the EM algorithm the correspondence of tracks is solved, and in Maximization-step the ‘unpaired’ tracks ratio are calculated. Finally, the track-to-track association is obtained by judgment. Monte carlo simulation demonstrates the effectiveness of the proposed approaches under different sensor biases, targets densities and detection probabilities.
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