This paper derives the optimal Bayesian processing of an out-of-sequence (OOS) set of measurements in continuous-time for multiple target tracking. We consider a multi-target system modelled in continuous time that is discretised at the time steps when we receive the measurements, which are distributed according to the standard point target model. All information about this system at the sampled time steps is provided by the posterior density on the set of all trajectories. This density can be computed via the continuous-discrete trajectory Poisson multi-Bernoulli mixture (TPMBM) filter. When we receive an OOS measurement, the optimal Bayesian processing performs a retrodiction step that adds trajectory information at the OOS measurement time stamp followed by an update step. After the OOS measurement update, the posterior remains in TPMBM form. We also provide a computationally lighter alternative based on a trajectory Poisson multi-Bernoulli filter. The effectiveness of the two approaches to handle OOS measurements is evaluated via simulations.
Applied Lyapunov Stability on Output Tracking Problem for a Class of Discrete-Time Linear Systems
Laboratory of Analysis Modeling and Simulation, Department of Mathematics and Computer Science, Faculty of Sciences Ben M’Sik, Hassan II University of Casablanca（卡萨布兰卡哈桑二世大学本米斯克理学院数学与计算机科学系分析、建模与仿真实验室）
$\text{H}_{\infty}$ Tracking Control via Variable Gain Gradient Descent-Based Integral Reinforcement Learning for Unknown Continuous Time Nonlinear System
Distributed average tracking for multiple reference signals with general linear dynamics
State Key Laboratory for Turbulence and Complex Systems, Department of Mechanics and Engineering Science, College of Engineering, Peking University（北京大学工学院力学与工程科学系湍流国家重点实验室）
Tsinghua University（清华大学）；Chinese Academy of Medical Sciences and Peking Union Medical College（中国医学科学院 北京协和医学院）；Institute of Semiconductors, Chinese Academy of Sciences（中国科学院半导体研究所）
