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Energy Logic (EL): A Novel Fusion Engine of Multi-modality Multi-agent Data/Information Fusion for Intelligent Surveillance Systems

机译:能源逻辑(EL):用于智能监控系统的新型多模式多主体数据/信息融合融合引擎

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摘要

The rapidly advancing hardware technology, smart sensors and sensor networks are advancing environment sensing. One major potential of this technology is Large-Scale Surveillance Systems (LS3) especially for, homeland security, battlefield intelligence, facility guarding and other civilian applications. The efficient and effective deployment of LS3 requires addressing number of aspects impacting the scalability of such systems. The scalability factors are related to: computation and memory utilization efficiency, communication bandwidth utilization, network topology (e.g., centralized, ad-hoc, hierarchical or hybrid), network communication protocol and data routing schemes; and local and global data/information fusion scheme for situational awareness. Although, many models have been proposed to address one aspect or another of these issues but, few have addressed the need for a multi-modality multi-agent data/information fusion that has characteristics satisfying the requirements of current and future intelligent sensors and sensor networks. In this paper, we have presented a novel scalable fusion engine for multi-modality multi-agent information fusion for LS3. The new fusion engine is based on a concept we call: Energy Logic. Experimental results of this work as compared to a Fuzzy logic model strongly supported the validity of the new model and inspired future directions for different levels of fusion and different applications.
机译:迅速发展的硬件技术,智能传感器和传感器网络正在推动环境传感。这项技术的主要潜力之一是大型监视系统(LS3),尤其适用于国土安全,战场情报,设施防护和其他民用应用。 LS3的有效部署需要解决影响此类系统可伸缩性的多个方面。可伸缩性因素涉及:计算和存储器利用效率,通信带宽利用,网络拓扑(例如,集中式,自组织,分层或混合),网络通信协议和数据路由方案;以及本地和全球的数据/信息融合方案,以实现态势感知。尽管已经提出了许多模型来解决这些问题的一个方面或另一个问题,但是很少有模型能够满足对多模式多主体数据/信息融合的需求,该融合具有能够满足当前和未来智能传感器和传感器网络要求的特性。 。在本文中,我们提出了一种新颖的可伸缩融合引擎,用于LS3的多模式多主体信息融合。新的融合引擎基于我们称为“能量逻辑”的概念。与模糊逻辑模型相比,这项工作的实验结果有力地支持了新模型的有效性,并启发了不同融合水平和不同应用的未来发展方向。

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