Efficient recursive distributed state estimation of hidden Markov models over unreliable networks
Efficient recursive distributed state estimation of hidden Markov models over unreliable networks
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DOI：
10.1007/s10514-019-09854-3
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发表时间：
2019-05
影响因子：
3.5
通讯作者：
A. Tamjidi;R. Oftadeh;S. Chakravorty;Dylan A. Shell
中科院分区：
文献类型：
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作者：
A. Tamjidi;R. Oftadeh;S. Chakravorty;Dylan A. Shell
我们考虑这样一个场景，其中一个感兴趣的过程，在一个由几个代理占据的环境中进化，通过马尔可夫模型被很好地描述。每个代理人都有当地的观点，只观察世界的一些有限的局部方面，但他们的总体任务是融合他们的数据，以构建一个完整的、全球的肖像。然而，问题在于它们的通信是不可靠的：网络链路可能会出现故障，数据包可能会被丢弃，而且通常网络可能会被长时间分割。然后，基本问题就变成了一致性问题，因为网络不同部分的代理从他们的观察中获得了新的信息，但只能与那些能够与他们通信的人共享这些信息。随着通信网络的变化，不同的观点可能会有分歧；挑战是协调这些差异。问题是，必须考虑相关性，以免一些传感器数据被重复计算，导致过度自信或偏见。为了解决这些问题，提出了一种新的基于隐马尔可夫模型的分布式状态估计的递归一致滤波算法。由于该算法具有可伸缩性、对网络故障的健壮性、能够处理非高斯过渡模型和观测模型，因此具有很强的通用性，因此非常适合多智能体环境和相关应用。至关重要的是，我们从来没有假设过全球对通信网络的了解。我们将该算法称为混合方法，因为同时使用了两个现有的部分：第一个，迭代保守融合用于在潜在相关的先验上达成共识，而第二个，基于Metropolis Hastings马尔可夫链的权重处理。为了更详细地理解模不完全通信的估计器性能的理论上限，我们引入了一种理想化的分布估计器。结果表明，在一定的一般条件下，所提出的混合方法指数收敛于理想分布估计量，尽管后者纯粹是概念性的，在实践中是不可实现的。通过一系列的模拟实验，对混合算法进行了广泛的评估，结果表明，该方法的性能优于竞争算法。
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.