Expectation-maximization-based infrared target tracking with time-varying extinction coefficient identification

Extinction coefficient (EC), as the key parameter of target intensity model, is assumed constant in classical infrared target tracking (IRTT) methods. However, it is a time-varying and state-coupled parameter related to complex atmosphere environment. To this end, this article proposes the problem of IRTT with time-varying EC identification. Different from the constant EC case whose solution is the measurement augmentation, the time-varying EC case brings out the new challenge: deep coupling between state estimation and parameter identification. In the expectation-maximization framework, this article derives the joint identification and estimation optimization scheme, where the Taylor expansion variance error of intensity model is also identified to adaptively compensate the nonlinear approximation. Simulation examples show that the proposed scheme has better estimation accuracy than the existing augmented extended Kalman filter.