This article investigates the distributed joint state and fault estimation issue for a class of nonlinear time-varying systems over sensor networks constrained by energy harvesting. It is assumed that data transmission between sensors requires energy consumption, and each sensor can harvest energy from the external environment. A Poisson process models the energy harvested by each sensor, and the sensor’s transmission decision depends on its current energy level. One can obtain the sensor transmission probability through a recursive calculation of the probability distribution of the energy level. Under such energy harvesting constraints, the proposed estimator only uses local and neighbor data to simultaneously estimate the system state and the fault, thereby establishing a distributed estimation framework. Moreover, the estimation error covariance is determined to possess an upper bound, which is minimized by devising energy-based filtering parameters. The convergence performance of the proposed estimator is analyzed. Finally, a practical example is presented to verify the usefulness of the main results.

中文翻译：

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收集受限传感器网络上基于分布式能量的估计

本文研究了受能量收集约束的传感器网络上的一类非线性时变系统的分布式联合状态和故障估计问题。假设传感器之间的数据传输需要消耗能量，并且每个传感器都可以从外部环境中获取能量。泊松过程对每个传感器收集的能量进行建模，传感器的传输决策取决于其当前的能量水平。通过能级概率分布的递归计算可以得到传感器传输概率。在这种能量收集约束下，所提出的估计器仅使用本地和邻居数据来同时估计系统状态和故障，从而建立分布式估计框架。此外，估计误差协方差被确定为具有上限，通过设计基于能量的滤波参数来最小化该上限。分析了所提出的估计器的收敛性能。最后给出一个实例来验证主要结果的实用性。