面向开放客户端-服务器网络的源可靠性加权观测器设计
Source Reliability Weighted Observer Design for Open Client Server Networks
AI总结：
针对开放客户端-服务器网络中活跃源不确定的状态估计问题，提出源可靠性加权观测器，经有限学习后可跟踪潜态，优于全活跃源观测方案。
AI中文摘要：
开放客户端-服务器网络中的状态估计颇具挑战性，因为活跃观测源集合随时间变化，且活跃源未必是潜态的有效传感器。本文研究一类客户端-服务器估计问题：固定但未知的状态一致传感类在活跃时测量潜态，而干扰源可能加入活跃集并生成不同信号类的观测；服务器可观测活跃源，但不知哪些代理观测到有效状态测量。本文提出源可靠性加权观测器：状态估计为标准固定先验加权最小二乘更新，分配给每个活跃观测的信息由从重复新息一致性中学习到的源级可靠性得分决定。数值结果表明，当考虑所有活跃源时，该观测器具有有限偏差；而所提观测器会先学习源可靠性，再在有限学习期后跟踪期望潜态。
英文摘要：
State estimation in open client server networks is challenging because the set of active observation sources changes over time, and active sources need not be valid sensors of the latent state. We study a client-server estimation problem in which a fixed but unknown state-consistent sensing class measures the latent state when active, while nuisance sources may arrive in the active set and generate observations from a different signal class. The server observes active sources, but it does not know which agents observe valid state measurements. We propose a source-reliability-weighted observer. The state estimate is a standard fixed-prior weighted least-squares update, but the information assigned to each active observation is determined by a source-level reliability score learned from repeated innovation consistency. Numerical results show that the observer, when all active sources are considered, has a finite bias, while the proposed observer initially learns source reliability and then tracks the desired latent state after a finite learning period.
