发表机构
University of Cyprus; Aalto University（塞浦路斯大学; 阿尔托大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文针对智能传感器与远程估计器间不可靠通信导致的远程状态估计问题，刻画了信息不对称下的信念结构，提出有限高斯混合表示，实现计算可处理，并通过仿真验证了其有效性。
AI 中文摘要
本文研究了智能传感器与远程估计器之间在不可靠的前向和反馈通信下的远程状态估计问题。当传感器无法完美重构远程估计器所维护的滤波状态时，网络两侧之间便会产生信息不对称。为分析这种不对称性，我们首先刻画了远程估计器所维护的内部状态及其在数据包接收过程中的演变。随后，通过基于其信息集的信念来捕捉传感器对该状态的不确定性，并在有噪声的确认反馈下推导出递归更新。最后，我们证明了由此产生的信念具有有限高斯混合表示，其分量数量随时间最多线性增长，从而确保了计算的可处理性。仿真结果展示了信息不对称对估计性能的影响，并验证了所提出框架的有效性。
英文摘要
In this paper, we study remote state estimation under unreliable forward and feedback communication between a smart sensor and a remote estimator. When the sensor cannot perfectly reconstruct the filtering state maintained by the remote estimator, an information asymmetry arises between the two sides of the network. To analyze this asymmetry, we first characterize the internal state maintained by the remote estimator and its evolution under the packet reception process. The sensor's uncertainty about this state is then captured through a belief conditioned on its information set, and we derive a recursive update under noisy acknowledgment feedback. Finally, we show that the resulting belief admits a finite Gaussian mixture representation whose number of components grows at most linearly over time, ensuring computational tractability. Simulation results illustrate the impact of information asymmetry on the estimation performance and demonstrate the effectiveness of the proposed framework.
Comments6 pages, conference paper
