首页> 外文会议>Conference on sensor fusion: Architectures, algorithms, and applications >Development of emergent processing loops as a system of systems concept
【24h】

Development of emergent processing loops as a system of systems concept

机译:将紧急处理循环开发为系统概念的系统

获取原文

摘要

Abstract: This paper describes an engineering approach toward implementing the current neuroscientific understanding of how the primate brain fuses, or integrates, 'information' in the decision-making process. We describe a System of Systems (SoS) design for improving the overall performance, capabilities, operational robustness, and user confidence in Identification (ID) systems and show how it could be applied to biometrics security. We use the Physio-associative temporal sensor integration algorithm (PATSIA) which is motivated by observed functions and interactions of the thalamus, hippocampus, and cortical structures in the brain. PATSIA utilizes signal theory mathematics to model how the human efficiently perceives and uses information from the environment. The hybrid architecture implements a possible SoS-level description of the Joint Directors of US Laboratories for Fusion Working Group's functional description involving 5 levels of fusion and their associated definitions. This SoS architecture propose dynamic sensor and knowledge-source integration by implementing multiple Emergent Processing Loops for predicting, feature extracting, matching, and Searching both static and dynamic database like MSTAR's PEMS loops. Biologically, this effort demonstrates these objectives by modeling similar processes from the eyes, ears, and somatosensory channels, through the thalamus, and to the cortices as appropriate while using the hippocampus for short-term memory search and storage as necessary. The particular approach demonstrated incorporates commercially available speaker verification and face recognition software and hardware to collect data and extract features to the PATSIA. The PATSIA maximizes the confidence levels for target identification or verification in dynamic situations using a belief filter. The proof of concept described here is easily adaptable and scaleable to other military and nonmilitary sensor fusion applications. !12
机译:摘要:本文介绍了实施目前对灵长类大脑融合或集成的决策过程中的热门脑保险丝或集成的“信息”的工程方法。我们描述了一种系统(SOS)设计系统,用于提高识别(ID)系统的整体性能,能力,操作鲁棒性和用户信心,并展示如何应用于生物识别安全性。我们使用了由观察到的丘脑,海马和皮质结构的观察功能和相互作用来激励的物理关联时间传感器集成算法(Patsia)。 Patsia利用信号理论数学来模拟人类如何有效地感知和使用环境的信息。混合架构实现了美国实验室联合董事的可能的SOS级描述,用于融合工作组的功能描述,涉及5级融合及其相关定义。此SOS架构通过实现多个紧急处理循环来提出动态传感器和知识源集成,以便预测,特征提取,匹配和搜索MSTAR PEMS循环等静态和动态数据库。在生物学上,这种努力通过在必要时使用海马以适当地使用丘脑,通过丘脑,以及适当地使用海马进行适当的时,通过丘脑和皮质来表现出这些目标。具体方法证明是商业上可用的扬声器验证和面部识别软件和硬件,以收集数据和提取特征到Patsia。使用信念过滤器,Patsia最大化目标识别或验证的置信水平。这里描述的概念证明易于适应和可扩展到其他军用和非限制性传感器融合应用。 !12

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号