Lossless Linear Transformation of Sensor Data for Distributed Estimation Fusion
Lossless Linear Transformation of Sensor Data for Distributed Estimation Fusion
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DOI：
10.1109/tsp.2010.2084574
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发表时间：
2011-01-01
影响因子：
5.4
通讯作者：
Li, X. Rong
中科院分区：
文献类型：
--
作者：
Duan, Zhansheng;Li, X. Rong
在分布式估计融合中，来自每个传感器的处理后的数据被发送到融合中心。通过对各传感器的原始测量值进行线性变换，提出了两种最优分布式融合算法。与现有的融合算法相比，它们有三个很好的性质。首先，它们是最优的，因为它们等价于最优集中式融合。其次，它们从每个传感器到融合中心的通信需求等于或小于集中式和大多数现有的分布式融合算法。第三，它们不需要估计误差协方差矩阵的逆，这是假设存在，但不能保证存在，在大多数现有的算法。因此，本文提出的算法可以应用于更多的场合。分析了这两种新算法的优缺点。一种可能的方法来降低新算法的计算复杂度，一个扩展的测量噪声的奇异协方差矩阵的情况下，和一些简单的系统的降低速率的通信情况下的扩展进行了讨论。
In distributed estimation fusion, processed data from each sensor is sent to the fusion center. By taking linear transformation of the raw measurements of each sensor, two optimal distributed fusion algorithms are proposed in this paper. Compared with existing fusion algorithms, they have three nice properties. First, they are optimal in the sense that they are equivalent to the optimal centralized fusion. Second, their communication requirements from each sensor to the fusion center are equal to or less than those of the centralized and most existing distributed fusion algorithms. Third, they do not need the inverses of estimation error covariance matrices, which are assumed to exist in most existing algorithms but can not be guaranteed to exist. So the proposed algorithms can be applied in more cases. Pros and cons of these two new algorithms are analyzed. A possible way to reduce the computational complexity of the new algorithms, an extension to the case of a singular covariance matrix of measurement noise, and an extension to the reduced-rate communication case for some simple systems are also discussed.