论文标题
无线传感器网络本地化问题的基于最小化的一阶方法交替
Alternating Minimization Based First-Order Method for the Wireless Sensor Network Localization Problem
论文作者
论文摘要
我们为无线传感器网络本地化问题提出了一种算法，该算法基于众所周知的交替最小化算法框架。我们从非平滑和非凸的最小化开始，然后将其转变为同等的平滑和非凸问题，这是我们研究的核心。这为全球收敛的新方法铺平了道路：目标函数值的序列不仅会收敛，而且位置估计的序列也收敛到唯一位置，这是相应（原始）目标函数的关键点。拟议的算法具有一系列完全分布到完全集中的实现的范围，这些实现都具有全球融合的属性。该算法在几种网络配置上进行了测试，并且显示出相对于现有方法，它可以在较短的时间内产生更准确的解决方案。
We propose an algorithm for the Wireless Sensor Network localization problem, which is based on the well-known algorithmic framework of Alternating Minimization. We start with a non-smooth and non-convex minimization, and transform it into an equivalent smooth and non-convex problem, which stands at the heart of our study. This paves the way to a new method which is globally convergent: not only does the sequence of objective function values converge, but the sequence of the location estimates also converges to a unique location that is a critical point of the corresponding (original) objective function. The proposed algorithm has a range of fully distributed to fully centralized implementations, which all have the property of global convergence. The algorithm is tested over several network configurations, and it is shown to produce more accurate solutions within a shorter time relative to existing methods.
