We consider a scenario in which a process of interest, evolving within an environment occupied by several agents, is well-described probablistically via a Markov model. The agents each have local views and observe only some limited partial aspects of the world, but their overall task is to fuse their data to construct an integrated, global portrayal. The problem, however, is that their communications are unreliable: network links may fail, packets can be dropped, and generally the network might be partitioned for protracted periods. The fundamental problem then becomes one of consistency as agents in different parts of the network gain new information from their observations but can only share this with those with whom they are able to communicate. As the communication network changes, different views may be at odds; the challenge is to reconcile these differences. The issue is that correlations must be accounted for, lest some sensor data be double counted, inducing overconfidence or bias. As a means to address these problems, a new recursive consensus filter for distributed state estimation on hidden Markov models is presented. It is shown to be well-suited to multi-agent settings and associated applications since the algorithm is scalable, robust to network failure, capable of handling non-Gaussian transition and observation models, and is, therefore, quite general. Crucially, no global knowledge of the communication network is ever assumed. We have dubbed the algorithm a Hybrid method because two existing pieces are used in concert: the first, iterative conservative fusion is used to reach consensus over potentially correlated priors, while consensus over likelihoods, the second, is handled using weights based on a Metropolis Hastings Markov chain. To attain a detailed understanding of the theoretical upper limit for estimator performance modulo imperfect communication, we introduce an idealized distributed estimator. It is shown that under certain general conditions, the proposed Hybrid method converges exponentially to the ideal distributed estimator, despite the latter being purely conceptual and unrealizable in practice. An extensive evaluation of the Hybrid method, through a series of simulated experiments, shows that its performance surpasses competing algorithms.

中文翻译：

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不可靠网络上隐马尔可夫模型的有效递归分布状态估计

我们考虑一种场景，其中通过马尔可夫模型概率性地描述了在由多个代理占据的环境中发展的目标过程。每个代理都有局部的看法，并且只观察世界的某些有限的局部方面，但是他们的总体任务是融合他们的数据以构建一个综合的全球形象。但是，问题在于它们的通信不可靠：网络链接可能会失败，数据包可能会丢失，并且通常网络可能会长时间分区。随着网络不同部分中的代理从他们的观察中获得新信息，但只能与他们能够与之通信的人员共享新信息，因此基本问题成为一致性的问题之一。随着通信网络的变化，不同的观点可能不一致。挑战是调和这些差异。问题是必须考虑相关性，以免重复计算某些传感器数据，从而引起过分自信或偏见。为了解决这些问题，提出了一种新的递归共识滤波器，用于对隐马尔可夫模型进行分布状态估计。由于该算法具有可伸缩性，对网络故障的鲁棒性，能够处理非高斯转换和观察模型，因此被证明非常适合多代理设置和相关的应用程序，因此非常通用。至关重要的是，从来没有假定对通信网络的全球了解。我们将算法称为“混合”方法，因为两个现有的部分共同使用：第一，迭代保守融合用于达成潜在相关先验的共识，而对可能性的共识（第二个）则使用基于Metropolis Hastings Markov链的权重进行处理。为了获得对估计器性能模不完美通信的理论上限的详细理解，我们介绍了一种理想化的分布式估计器。结果表明，在某些一般条件下，尽管理想情况下的纯估计量只是概念上的，在实践中是无法实现的，但所提出的混合方法却以指数形式收敛于理想的分布估计量。通过一系列模拟实验对混合方法的广泛评估表明，其性能优于竞争算法。为了获得对估计器性能模不完美通信的理论上限的详细理解，我们介绍了一种理想化的分布式估计器。结果表明，在一定的一般条件下，尽管理想值估计量仅仅是概念上的，在实践中是无法实现的，但所提出的混合方法却以指数形式收敛于理想的分布估计量。通过一系列模拟实验对混合方法的广泛评估表明，其性能优于竞争算法。为了获得对估计器性能模不完美通信的理论上限的详细理解，我们引入了一种理想的分布式估计器。结果表明，在某些一般条件下，尽管理想情况下的纯估计量只是概念上的，在实践中是无法实现的，但所提出的混合方法却以指数形式收敛于理想的分布估计量。通过一系列模拟实验对混合方法的广泛评估表明，其性能优于竞争算法。