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首页> 外文期刊>IEEE Transactions on Aerospace and Electronic Systems >Conditionally Markov Modeling and Optimal Estimation for Trajectory With Waypoints and Destination
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Conditionally Markov Modeling and Optimal Estimation for Trajectory With Waypoints and Destination

机译：条件性马尔可夫建模与航路点和目的地轨迹的建模和最优估计

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摘要

On a grand scale, motion trajectories are usually defined by an origin, a sequence of waypoints, and a destination. A typical example is in air traffic management (ATM), where a flight from an origin passes several waypoints and arrives at a destination. The origin, the waypoints, and the destination contain useful information for trajectory modeling. On the other hand, due to trajectory design criteria and ATM restrictions and requirements, there are long-range dependencies in a flight trajectory. Such dependencies can be modeled by taking the origin, waypoints, and destination into account in trajectory modeling. In this article, we propose a class of conditionally Markov (CM) sequences to model such trajectories with long-range dependencies. First, we define a general CM sequence as a foundation. Then, we discuss its special cases for different scenarios. We derive dynamic models of these CM sequences in the Gaussian case. We show how parameters of the models can be learned from data or designed. We also justify the use of the proposed CM models for trajectory modeling. In addition, we obtain optimal filters and predictors for different models. Simulation demonstrations are given.
机译：在大规模上，运动轨迹通常由起源，一系列航点和目的地定义。典型的例子是空中交通管理（ATM），其中来自原点的航班通过了多个航点并到达目的地。原点，航点和目的地包含用于轨迹建模的有用信息。另一方面，由于轨迹设计标准和ATM限制和要求，飞行轨迹中存在远程依赖性。可以通过在轨迹建模中考虑起源，航点和目的地来建模这些依赖项。在本文中，我们提出了一类有条件的Markov（CM）序列，以模拟具有远程依赖性的这种轨迹。首先，我们将一般CM序列定义为基础。然后，我们讨论其特殊情况的不同情景。我们在高斯案例中导出了这些CM序列的动态模型。我们展示了如何从数据或设计的模型参数。我们还证明了使用所提出的CM模型进行轨迹建模。此外，我们获得不同型号的最佳滤波器和预测因子。给出了模拟演示。

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