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.

中文翻译：

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基于交替最小化的无线传感器网络定位问题的一阶方法

我们针对无线传感器网络定位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。我们从一个非光滑非凸最小化开始，并将其转化为等效的光滑非凸问题，这是我们研究的核心。这为全局收敛的新方法铺平了道路：不仅目标函数值的序列收敛，而且位置估计的序列也收敛到唯一位置，该位置是对应（原始）目标函数的临界点. 所提出的算法有一系列完全分布式到完全集中的实现，它们都具有全局收敛的特性。该算法在多种网络配置上进行了测试，