传感器网络上的非线性系统的加权融合估计
Weighted Scheduling Fusion Estimation for Nonlinear Stochastic System over Sensor Networks
摘要: 考虑到一类具有随机发生的扇区非线性的离散时间系统的概率依赖加权调度，本文关注融合估计问题。假定损耗测量现象是随机发生的，并且损耗概率随时间变化，可以实时测量其下限和上限安全极限。所解决的加权调度滤波器问题的目标计划设计一个具有加权调度矩阵的估计器，以便针对外部噪声干扰和可容许的随机发生的非线性。借助Lyapunov类型分析方法，可以获得充分条件，以保证加权调度估计误差系统的稳定性。最后，使用示例性的仿真来证明提案设计方案的有效性。
Abstract: This paper is concerned with fusion estimation problem in view of the probability-dependent weighted scheduling for a class of discrete-time systems with stochastically occurring sector nonlinearities. It is assumed that the loss measurement phenomenon occurs randomly, the loss probability varies with time, and its lower and upper safety limits can be gauged in real time. The objective of the addressed weighted scheduling filter problem plans to design an estimator with weighted scheduled matrices, for external noise disturbances and admissible random occurrence of nonlinearity. With the help of Lyapunov type analysis approach, sufficient conditions can be gotten to guarantee the stability of the weighted scheduling estimation error system. At last, an illustrative simulation is used to certify the effectiveness of the proposed design scheme.
文章引用：石也明, 肖寒臣, 石玉成. 传感器网络上的非线性系统的加权融合估计[J]. 应用数学进展, 2021, 10(4): 1141-1152. https://doi.org/10.12677/AAM.2021.104124

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