ARL和ASCTA的网络化微传感器研究

A. Filipov, N. Srour, M. Falco, N.H. Nashua
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引用次数: 2

摘要

ARL与先进传感器协作技术联盟(ASCTA)联合开展了一项研究计划,旨在开发一种技术,使分布式无人值守地面传感器(UGS)形成临时网络,这种网络将是廉价的,并且能够在一个电池上一次运行数月。这些网络将协同处理多模态传感器数据,以实现车辆和人员的多目标检测、分类和跟踪(DCT)。为了最好地实现这一目标,正在开发一种能够在受限带宽条件下运行的模块化、可扩展和健壮的分散融合架构,该架构将跨系统层次结构的所有级别执行数据、特征和信息级融合。将开发算法来自主分配资源以优化系统性能;这些将包括自校准和定位,目标切换传感器提示,电源管理以及DCT性能的整体改进。这将允许快速部署UGS油田,提供有关大面积未使用区域的信息。最后,系统建模和仿真将有助于优化整体网络性能、成本、运行寿命和带宽使用。这将确定新的所需传感器模式和传感器改进的必要领域。此外,在那些预计会有大量处理负荷的领域,将开发工具来利用电力/能源技术来有效地实现复杂的算法。将报告所有这些领域的进展,并确定新的研究机会。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Networked microsensor research at ARL and the ASCTA
ARL in conjunction with the Advanced Sensors Collaborative Technology Alliance (ASCTA) has embarked on a research program to develop technology that enables distributed Unattended Ground Sensors (UGS) to form ad hock networks which will be inexpensive and will be able to operate for months at a time on a single battery. These networks will cooperatively process multi-modal sensor data to achieve multi-target Detection, Classification and Tracking (DCT) of vehicles and people. To best accomplish this goal, a modular, scalable, and robust decentralized fusion architecture capable of operating under constrained bandwidth conditions is being developed that will perform data, feature, and information level fusion across all levels of the system hierarchy. Algorithms will be developed to autonomously allocate resources to optimize system performance; these will include self-calibration and localization, target handoff sensor cueing, power management, and overall improvement in performance of DCT. This will allow for rapid deployment of UGS fields that provide information about large unoccupied areas. Finally, system modeling and simulation will help optimize overall network performance, cost, operating life and bandwidth usage. This will identify new required sensor modalities and areas where sensor improvements are necessary. Furthermore in those areas where significant processing load is anticipated, tools will be developed to exploit power/energy techniques for efficiently implementing complex algorithms. Progress will be reported in all of these areas and new research opportunities will be identified.
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