Infrared maneuvering target tracking based on IMM-PF with adaptive observation model

For the problem of infrared target tracking, we propose a mixture observation model, which can describe both the gradual intensity variation and sudden disappearance of target pixels, and use an online EM algorithm to update the model parameters. The proposed adaptive observation model is incorporated with the interacting multiple model particle filter (IMM-PF) for target tracking. Finally, we extend the algorithm to multiple targets tracking by introducing a likelihood function based on the probabilistic exclusion principle. Experimental and simulation results demonstrate the robustness of our algorithm.