论文标题
分布式自适应信号和特征融合问题的统一算法框架 - 第二部分：收敛属性
A Unified Algorithmic Framework for Distributed Adaptive Signal and Feature Fusion Problems -- Part II: Convergence Properties
论文作者
论文摘要
本文研究了分布式自适应信号融合（DASF）算法的收敛条件和特性，该算法本身是在“第一部分”伴随论文中引入的。 DASF算法可用于以分布式方式求解线性信号和特征融合优化问题，并且特别适合解决无线传感器网络中遇到的空间滤波优化问题。提供收敛条件和结果以及严格的证明和分析以及它们适用的各种示例问题。此外，我们描述了可以添加到DASF算法中的程序，以确保在某些技术收敛条件不满足的特定情况下进行收敛。
This paper studies the convergence conditions and properties of the distributed adaptive signal fusion (DASF) algorithm, the framework itself having been introduced in a `Part I' companion paper. The DASF algorithm can be used to solve linear signal and feature fusion optimization problems in a distributed fashion, and is in particular well-suited for solving spatial filtering optimization problems encountered in wireless sensor networks. The convergence conditions and results are provided along with rigorous proofs and analyses, as well as various example problems to which they apply. Additionally, we describe procedures that can be added to the DASF algorithm to ensure convergence in specific cases where some of the technical convergence conditions are not satisfied.
