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.

我们介绍了一种分布式算法，称为噪声鲁棒的分布式最大共识（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。通过将最大共识问题重新定义为分布式优化问题，我们使用交替方向乘法器方法进行求解。与现有算法依赖多组受噪声干扰的估计不同，RD-MC 采用单组估计，提高了鲁棒性和效率。为了进一步减轻链路噪声的影响并提高鲁棒性，我们对局部估计应用移动平均。通过广泛的仿真，我们证明了 RD-MC 相比现有的最大共识算法对通信链路噪声更具鲁棒性。