具有决策反馈的并联分散传感器网络性能研究

S. Kopparthi, B. Natarajan
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引用次数: 2

摘要

在大多数先前的研究中,分布式传感器网络通常被认为是内存较少的。在本文中,我们考虑了一个在传感器级和融合中心都有反馈的分布式传感器网络。具体来说,我们分析了(1)一个局部反馈系统(LFS),其中最新的局部决策被反馈到相应的局部传感器;(2)局部和全局反馈系统1 (LGFS1),将最新的局部决策反馈给相应的局部传感器,并将最新的全局决策反馈给融合中心;(3)局部和全局反馈系统2 (LGFS2),除了将最新的局部决策反馈给相应的局部传感器外,还将最新的全局决策反馈给所有的局部传感器和融合中心。对于所有情况,我们推导了决策规则,并通过仿真比较了全局误差概率。我们表明,在无误差通道中,任何形式的反馈相对于无反馈系统都能提高GPE性能。然而,将全局决策反馈给局部传感器不仅消耗资源,而且在反馈方案中性能最差
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Performance of Parallel Decentralized Sensor Network with Decision Feedback
In most previous studies, distributed sensor networks are typically assumed to be memory less. In this paper, we consider a distributed sensor network with feedback at both the sensor level as well as at the fusion center. Specifically, we analyze (1) a local feedback system (LFS), where the most recent local decision is fed back to its corresponding local sensor; (2) a local and global feedback system 1 (LGFS1) where the most recent local decision is fed back to its corresponding local sensor and the most recent global decision is fed back to the fusion center, and (3) a local and global feedback system 2 (LGFS2), where the most recent global decision is fed back to all the local sensors and to the fusion center in addition to the local decision being fed back to its corresponding local sensor. For all the cases, we derive the decision rule and compare the global probability of error using simulations. We show that in an error-free channel, any form of feedback improves GPE performance relative to no feedback system. However, feeding the global decision back to local sensors not only drains resources but also provides the worst performance among feedback schemes
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