Recursive joint track-to-track association and sensor nonlinear bias estimation based on generalized Bayes risk

Track-to-track association and sensor bias estimation are two important problems in multi-target multi-sensor tracking system. Track-to-track association becomes more complex in the presence of sensor bias and incorrect track association will lead to poor bias estimation results. Solving these two problems jointly would be attractive. This paper proposes a recursive joint track-to-track association and nonlinear bias estimation algorithm based on the generalized Bayes risk. The proposed algorithm and the conventional association-then-estimation algorithm are compared with the Monte-Carlo simulation. Simulation results show that the proposed algorithm has better track association and bias estimation performance than the conventional algorithm.