We introduce a distributed algorithm, termed noise-robust distributed maximum consensus (RD-MC), for estimating the maximum value within a multi-agent network in the presence of noisy communication links. Our approach entails redefining the maximum consensus problem as a distributed optimization problem, allowing a solution using the alternating direction method of multipliers. Unlike existing algorithms that rely on multiple sets of noise-corrupted estimates, RD-MC employs a single set, enhancing both robustness and efficiency. To further mitigate the effects of link noise and improve robustness, we apply moving averaging to the local estimates. Through extensive simulations, we demonstrate that RD-MC is significantly more robust to communication link noise compared to existing maximum-consensus algorithms.

翻译：本文提出了一种分布式算法，称为噪声鲁棒分布式最大一致性（RD-MC），用于在存在噪声通信链路的多智能体网络中估计最大值。我们的方法将最大一致性问题重新定义为分布式优化问题，从而可以利用交替方向乘子法求解。与现有依赖多组噪声污染估计值的算法不同，RD-MC仅采用单组估计值，同时增强了鲁棒性和效率。为了进一步减轻链路噪声的影响并提高鲁棒性，我们对局部估计值应用了移动平均。通过大量仿真实验，我们证明与现有的最大一致性算法相比，RD-MC对通信链路噪声具有显著更强的鲁棒性。