无线传感器网络分布式动态状态估计的一致性和置信度研究
On the Consistency and Confidence of Distributed Dynamic State Estimation in Wireless Sensor Networks
摘要：
研究了无线传感器网络中分布式动态状态估计问题。强调了局部估计的两个重要性质，即相合性和置信度。一方面，必须保证近似误差协方差的一致性，即近似误差协方差是真实未知误差协方差的下界，从而使估计不会过于自信。另一方面，由于置信度表示估计的准确性，因此估计应该尽可能地置信。我们首先分析了两种不同的信息融合策略的情况下，信息源，分别与不相关的错误和未知的，但相关的错误。在此基础上，提出了一种分布式混合信息融合算法，每个智能体不仅利用自身获得的信息，还利用通过通信从邻居获得的信息。该算法不仅保证了估计的一致性，而且以更有效的方式利用了可用的信息源，从而提高了置信度。此外，所提出的算法是全分布的，并保证收敛的充分条件制定。与现有算法的比较。
英文摘要：
The problem of distributed dynamic state estimation in wireless sensor networks is studied. Two important properties of local estimates, namely, the consistency and confidence, are emphasized. On one hand, the consistency, which means that the approximated error covariance is lower bounded by the true unknown one, has to be guaranteed so that the estimate is not over-confident. On the other hand, since the confidence indicates the accuracy of the estimate, the estimate should be as confident as possible. We first analyze two different information fusion strategies used in the case of information sources with, respectively, uncorrelated errors and unknown but correlated errors. Then a distributed hybrid information fusion algorithm is proposed, where each agent uses the information obtained not only by itself, but also from its neighbors through communication. The proposed algorithm not only guarantees the consistency of the estimates, but also utilizes the available information sources in a more efficient manner and hence improves the confidence. Besides, the proposed algorithm is fully distributed and guarantees convergence with the sufficient condition formulated. The comparisons with existing algorithms are shown.
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