论文标题
传感器网络中的最佳耐故障数据融合：基本限制和有效算法
Optimal Fault-Tolerant Data Fusion in Sensor Networks: Fundamental Limits and Efficient Algorithms
论文作者
论文摘要
考虑了传感器网络上下文中的分布式估计，在给分布式试剂的情况下，给出了一组传感器测量，并承担估计目标变量的任务。假定传感器子集有故障。目的是最小化i）每个节点处的均方根估计误差（准确性目标），ii）每对节点处的估计值之间的均方距离（共识目标）。结果表明，前者和后一个目标之间存在固有的权衡。假设有一个一般的随机模型，通过可计算的优化问题来表征传感器融合算法优化此权衡的特征，并且获得了可实现的准确性传记损失的CRAMER-RAO类型下限。找到最佳传感器融合算法在计算上是复杂的。为了解决这个问题，引入了一般的低复杂性brooks-iyengar算法，并将其在准确性和共识目标方面与通过各种情况的案例研究模拟进行的最佳线性估计器进行了比较。
Distributed estimation in the context of sensor networks is considered, where distributed agents are given a set of sensor measurements, and are tasked with estimating a target variable. A subset of sensors are assumed to be faulty. The objective is to minimize i) the mean square estimation error at each node (accuracy objective), and ii) the mean square distance between the estimates at each pair of nodes (consensus objective). It is shown that there is an inherent tradeoff between the former and latter objectives. Assuming a general stochastic model, the sensor fusion algorithm optimizing this tradeoff is characterized through a computable optimization problem, and a Cramer-Rao type lower bound for the achievable accuracy-consensus loss is obtained. Finding the optimal sensor fusion algorithm is computationally complex. To address this, a general class of low-complexity Brooks-Iyengar Algorithms are introduced, and their performance, in terms of accuracy and consensus objectives, is compared to that of optimal linear estimators through case study simulations of various scenarios.
