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首页> 外文会议>Ground/air multisensor interoperablity, integration, and networking for persistent ISR IV ; Open architecture/open business model net-centric systems and defense transformation 2013 >Air Route Selection for improved Air-to-Ground Situation Assessment
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Air Route Selection for improved Air-to-Ground Situation Assessment

机译：选择航线以改善空地情况评估

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Air-to-Ground Situation Assessment (SA) requires gathering information on the entities evolving on the ground (e.g., people, vehicles), and inferring the relations among them and their final intent. Several airborne sensor data might concur in the compilation of such high-level picture, which is aimed at identifying threats and promptly raising alarms. However, this process is intrinsically prone to errors: as the evidence - provided to the SA algorithm - originates from noisy sensor observations, the final outcome is also affected by uncertainty. High-level inferred variables, such as "Situation" and "Threat Level", can be seen as error-affected, incomplete estimates of the ground truth, hence they are subject to improvement by properly steering the data acquisition process. In this paper we address the problem of optimizing the air route of the sensing platform, in order to reduce the number of false declarations or the delay in threat declaration in the SA stage. Specifically, we consider the problem of detecting a hostile behavior between pairs of ground targets by exploiting track data generated from airborne bearings-only measurements. We model the optimization problem with a sequential Markov Decision Process (MDP): the platform sequentially selects the best maneuver (i.e., its acceleration vector) in order to maximize the total reward over an infinite horizon. We define the potential contribution of an action as a function of the expected environmental conditions (e.g., obscurations of the line-of-sight) and the improvement of the localization accuracy achievable for the tracked objects. We demonstrate that following the optimized trajectory the delay in the declaration of a hostile behavior between two targets is reduced at the same erroneous declaration rate.
机译：空对地情况评估（SA）要求收集有关地面上不断发展的实体（例如人，车辆）的信息，并推断它们之间的关系及其最终意图。在此类高级别图片的汇编中，可能会同意一些机载传感器数据，其目的是识别威胁并及时发出警报。但是，此过程本质上很容易出错：因为提供给SA算法的证据来自嘈杂的传感器观测结果，所以最终结果也受到不确定性的影响。可以将高级推断变量（例如“状况”和“威胁级别”）视为受错误影响，对地面真相的不完全估计，因此可以通过适当地控制数据采集过程来进行改进。在本文中，我们解决了优化感测平台航路的问题，以减少在SA阶段错误声明的数量或威胁声明的延迟。具体而言，我们考虑了通过利用仅机载轴承测量值生成的跟踪数据来检测成对的地面目标之间敌对行为的问题。我们使用顺序马尔可夫决策过程（MDP）对优化问题进行建模：平台顺序选择最佳操作（即其加速度矢量），以在无限的范围内最大化总回报。我们根据预期的环境条件（例如视线的遮挡）和可跟踪对象可实现的定位精度的改善来定义动作的潜在贡献。我们证明，遵循优化的轨迹，两个目标之间的敌对行为的声明延迟以相同的错误声明率降低。

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