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.

中文翻译：

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基于期望最大化的红外目标跟踪，并具有随时间变化的消光系数识别

在经典的红外目标跟踪（IRTT）方法中，消光系数（EC）作为目标强度模型的关键参数被假定为常数。但是，它是与复杂大气环境相关的时变和状态耦合参数。为此，本文提出了随时间变化的EC识别的IRTT问题。与解决方案是增加测量的恒定EC案例不同，时变EC案例带来了新的挑战：状态估计与参数识别之间的深层耦合。在期望最大化框架中，本文推导了联合识别和估计优化方案，其中强度模型的泰勒展开方差误差也被识别出来，以自适应地补偿非线性逼近。