While autonomous systems can be used for a variety of beneficial applications, they can also be used for malicious intentions and it is mandatory to disrupt them before they act. So, an accurate trajectory inference algorithm is required for monitoring purposes that allows to take appropriate countermeasures. This article presents a closed-loop output error approach for trajectory inference of a class of linear systems. The approach combines the main advantages of state estimation and parameter identification algorithms in a complementary fashion using online data and an estimated model, which is constructed by the state and parameter estimates, that inform about the physics of the system to infer the followed noise-free trajectory. Exact model matching and estimation error cases are analyzed. A composite update rule based on a least-squares rule is also proposed to improve robustness and parameter and state convergence. The stability and convergence of the proposed approaches are assessed via the Lyapunov stability theory under the fulfilment of a persistent excitation condition. Simulation studies are carried out to validate the proposed approaches.

中文翻译：

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使用在线数据进行基于物理的轨迹推断的闭环输出误差方法

虽然自治系统可用于各种有益的应用，但它们也可用于恶意目的，因此必须在它们采取行动之前对其进行破坏。因此，需要一种准确的轨迹推理算法来实现监控目的，以便采取适当的对策。本文介绍了一种用于一类线性系统轨迹推理的闭环输出误差方法。该方法使用在线数据和由状态和参数估计构建的估计模型，以互补的方式结合了状态估计和参数识别算法的主要优点，该模型告知系统的物理特性以推断随后的无噪声弹道。分析了精确模型匹配和估计错误的情况。还提出了一种基于最小二乘规则的复合更新规则，以提高鲁棒性以及参数和状态收敛性。所提出方法的稳定性和收敛性通过 Lyapunov 稳定性理论在满足持续激励条件下进行评估。进行了仿真研究以验证所提出的方法。