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首页> 外文期刊>International Journal of Sensor Networks >Two-level robust sequential covariance intersection fusion Kalman predictors over clustering sensor networks with uncertain noise variances
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Two-level robust sequential covariance intersection fusion Kalman predictors over clustering sensor networks with uncertain noise variances

机译：具有不确定噪声方差的聚类传感器网络上的两级鲁棒顺序协方差相交融合卡尔曼预测器

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This paper studies the problem of designing two-level robust sequential covariance intersection (SCI) fusion Kalman predictors for the clustering sensor networks with noise variances uncertainties. The sensor networks consist of many clusters, which are partitioned by the nearest neighbour rule. According to the minimax robust estimation principle, based on the worst-case conservative clustering sensor network with the conservative upper bound of noise variances, the two-level SCI fusion Kalman predictors are presented where the first level is the local SCI fusion predictors and the second level is the global SCI fusion predictor. This two-level fused structure can significantly reduce the communicational burden and save the energy sources. The robustness of the local and fused Kalman predictors is proved based on the Lyapunov equation method, and the robust accuracy relations are proved. A simulation example verifies the correctness and effectiveness of the proposed robust SCI predictor.
机译：本文研究了为具有噪声方差不确定性的聚类传感器网络设计两级鲁棒顺序协方差相交（SCI）融合卡尔曼预测器的问题。传感器网络由许多群集组成，这些群集按最近邻居规则进行分区。根据最小极大鲁棒估计原理，基于具有噪声方差保守上限的最坏情况保守聚类传感器网络，提出了两级SCI融合卡尔曼预测器，其中第一级是局部SCI融合预测器，第二级是局部SCI融合预测器。级别是全球SCI融合预测指标。这种两级融合结构可以显着减少通信负担并节省能源。基于Lyapunov方程方法证明了局部和融合卡尔曼预测变量的鲁棒性，并证明了鲁棒精度关系。仿真示例验证了所提出的鲁棒SCI预测器的正确性和有效性。

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