网络中具有贝叶斯二次博弈融合的卡尔曼滤波

Muyuan Zhai, Hui Feng, Yuanyuan Tan, Bo Hu
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引用次数: 0

摘要

网络中的分布式滤波是网络信号处理领域的一个基本问题。每个节点依靠私有观测和来自网络的融合信息来估计或跟踪某个未知状态。网络融合通常是一种网络上的交互方式,节点之间可以相互学习,相互决策。与传统方法不同,我们使用贝叶斯网络博弈作为融合工具构建分布式过滤器,其中所有节点交换其最佳策略而不是交换局部估计器。该算法是信号处理和网络博弈理论的结合,可以推广到更一般的信号处理和决策模型中。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Kalman filters with Bayesian quadratic game fusion in networks
Distributed filtering in network is a fundamental problem in the field of network signal processing. Each node estimates or tracks some unknown state relying on the private observation and the fusion information from the network. Network fusion is generally a way of interaction over network, by which nodes can learn from each other and make decision mutually. Unlike conventional methods, we construct a distributed filter using Bayesian network game as a fusion tool, where all the nodes exchange their best strategies instead of exchanging local estimators. The proposed algorithm is a coalition of signal processing and game theory in network, which can be extended to more general signal processing and decision making models.
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