论文标题
通过多传感器在线内核标量量化的非参数分散检测和稀疏传感器选择
Nonparametric Decentralized Detection and Sparse Sensor Selection via Multi-Sensor Online Kernel Scalar Quantization
论文作者
论文摘要
信号分类问题出现在各种应用中，并且其需求只有预计会增长。在本文中，我们专注于无线传感器网络信号分类设置，每个传感器都将量化的信号转发到要分类的融合中心。我们的主要目标是训练决策功能和跨传感器的量化器，以在线方式最大化分类性能。此外，我们对使用边缘化的加权内核方法进行稀疏传感器的选择感兴趣，以通过禁用较低的可靠传感器来提高网络资源效率，对分类性能的影响最小。要实现我们的目标，我们开发了多传感器在线内核标量量化（MSOKSQ）学习策略，该学习策略可在融合中心的传感器输出中运行。我们的理论分析揭示了所提出的算法如何影响传感器之间的量化器。此外，我们通过研究其与批处理学习的关系来对我们的在线学习方法进行融合分析。我们在不同的分类和传感器网络设置下进行数值研究，这些研究证明了优化MSOKSQ的不同组件和鲁棒性到减少所选传感器数量的准确性提高。
Signal classification problems arise in a wide variety of applications, and their demand is only expected to grow. In this paper, we focus on the wireless sensor network signal classification setting, where each sensor forwards quantized signals to a fusion center to be classified. Our primary goal is to train a decision function and quantizers across the sensors to maximize the classification performance in an online manner. Moreover, we are interested in sparse sensor selection using a marginalized weighted kernel approach to improve network resource efficiency by disabling less reliable sensors with minimal effect on classification performance.To achieve our goals, we develop a multi-sensor online kernel scalar quantization (MSOKSQ) learning strategy that operates on the sensor outputs at the fusion center. Our theoretical analysis reveals how the proposed algorithm affects the quantizers across the sensors. Additionally, we provide a convergence analysis of our online learning approach by studying its relationship to batch learning. We conduct numerical studies under different classification and sensor network settings which demonstrate the accuracy gains from optimizing different components of MSOKSQ and robustness to reduction in the number of sensors selected.
