To address track-to-track association problem for aircraft platforms in complex condition

where targets are distributed closely

sensor biases are time-varied

and different sensors report different targets

an anti-bias track-to-track association algorithm based on distance hierarchical clustering is proposed according to the statistical characteristics of Gaussian random vectors.Equivalent measurement equation for moving platform is firstly derived

linear relationship between state estimates and real states

sensor biases

measurement errors is established based on Taylor series expansion

distance vector is obtained based on real state cancellation

and homologous tracks are extracted based on distance vectors hierarchical clustering.Adaptability experiments are established based on three factors including different targets densities

random errors and sensor biases.Monte Carlo simulations demonstrate significant improvements of association accuracy and complex condition adaptability of the proposed algorithm compared with the classical algorithm based on the reference topology feature (RET).