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首页> 外文会议>Unmanned Systems Technology IX; Proceedings of SPIE-The International Society for Optical Engineering; vol.6561 >Towards distributed ATR using Subject Logic combination rules with a swarm of UAVs
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Towards distributed ATR using Subject Logic combination rules with a swarm of UAVs

机译：使用主题逻辑组合规则和大量无人机实现分布式ATR

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In this paper, we present our initial findings demonstrating a cost-effective approach to Aided Target Recognition (ATR) employing a swarm of inexpensive Unmanned Aerial Vehicles (UAVs). We call our approach Distributed ATR (DATR). Our paper describes the utility of DATR for autonomous UAV operations, provides an overview of our methods, and the results of our initial simulation-based implementation and feasibility study. Our technology is aimed towards small and micro UAVs where platform restrictions allow only a modest quality camera and limited on-board computational capabilities. It is understood that an inexpensive sensor coupled with limited processing capability would be challenged in deriving a high probability of detection (P_d) while maintaining a low probability of false alarms (P_(fa)). Our hypothesis is that an evidential reasoning approach to fusing the observations of multiple UAVs observing approximately the same scene can raise the P_d and lower the P_(fa) sufficiently in order to provide a cost-effective ATR capability. This capability can lead to practical implementations of autonomous, coordinated, multi-UAV operations. In our system, the live video feed from a UAV is processed by a lightweight real-time ATR algorithm. This algorithm provides a set of possible classifications for each detected object over a possibility space defined by a set of exemplars. The classifications for each frame within a short observation interval (a few seconds) are used to generate a belief statement. Our system considers how many frames in the observation interval support each potential classification. A definable function transforms the observational data into a belief value. The belief value, or opinion, represents the UAV's belief that an object of the particular class exists in the area covered during the observation interval. The opinion is submitted as evidence in an evidential reasoning system. Opinions from observations over the same spatial area will have similar index values in the evidence cache. The evidential reasoning system combines observations of similar spatial indexes, discounting older observations based upon a parameterized information aging function. We employ Subjective Logic operations in the discounting and combination of opinions. The result is the consensus opinion from all observations that an object of a given class exists in a given region.
机译：在本文中，我们介绍了我们的初步发现，这些结果证明了使用大量廉价的无人机（UAV）的具有成本效益的辅助目标识别（ATR）方法。我们称其为分布式ATR（DATR）。我们的论文描述了DATR在自主无人机操作中的实用性，概述了我们的方法，以及基于初始仿真的实施和可行性研究的结果。我们的技术针对小型和微型UAV，这些无人机的平台限制仅允许中等质量的摄像机和有限的机载计算能力。可以理解的是，在保持低错误警报概率（P_（fa））的同时，推导高检测概率（P_d）的同时，将挑战廉价的传感器和有限的处理能力。我们的假设是，将多个UAV观察到的大致相同场景融合在一起的证据推理方法可以充分提高P_d并降低P_（fa），从而提供具有成本效益的ATR能力。这种能力可以导致自主，协调，多UAV操作的实际实施。在我们的系统中，来自无人机的实时视频馈送由轻量级实时ATR算法处理。该算法在由一组示例定义的可能性空间上，为每个检测到的对象提供了一组可能的分类。在较短的观察间隔（几秒钟）内，每个帧的分类用于生成信念陈述。我们的系统考虑观察间隔中有多少帧支持每个潜在分类。可定义的函数将观测数据转换为置信度值。信念值或观点表示无人机的信念，即在观察间隔内所覆盖区域中存在特定类别的对象。该意见在证据推理系统中作为证据提交。来自相同空间区域的观察意见将在证据缓存中具有相似的索引值。证据推理系统结合了相似空间索引的观测值，并基于参数化信息老化函数对较旧的观测值进行了折现。我们将主观逻辑运算用于观点的打折和组合。结果是所有观察结果的一致意见，即给定区域中存在给定类别的对象。

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