基于集员滤波的多智能体系统协同状态估计
Set-Membership Filtering-Based Cooperative State Estimation for Multi-Agent Systems
AI总结：
本文研究多智能体系统的协同状态估计问题，提出基于集员滤波的集中式与分布式约束Zonotopic算法，使智能体仅利用局部信息与通信生成自身状态估计，仿真验证了算法的有效性。
摘要：
本文研究多智能体系统的协同状态估计问题。每个智能体配备绝对和相对测量。研究目的是利用集员滤波（SMF），使每个智能体仅通过局部测量信息及与邻居智能体的局部通信生成自身状态估计。为解决此问题，我们分析了集中式SMF框架作为分布式SMF的基准，并提出一种有限视界方法，称为受OIT启发的集中式约束Zonotopic算法。此外，我们提出了分布式集员滤波（SMFing）框架，并开发了分布式约束Zonotopic算法。最后，仿真验证了理论结果，表明所提算法能有效估计每个智能体的状态。
英文摘要：
In this article, we focus on the cooperative state estimation problem of a multi-agent system. Each agent is equipped with absolute and relative measurements. The purpose of this research is to make each agent generate its own state estimation with only local measurement information and local communication with neighborhood agents using Set Membership Filter(SMF). To handle this problem, we analyzed centralized SMF framework as a benchmark of distributed SMF and propose a finite-horizon method called OIT-Inspired centralized constrained zonotopic algorithm. Moreover, we put forward a distributed Set Membership Filtering(SMFing) framework and develop a distributed constained zonotopic algorithm. Finally, simulation verified our theoretical results, that our proposed algorithms can effectively estimate the state of each agent.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
