论文标题
资源约束下的分布式事件触发的非线性融合估计
Distributed Event-Triggered Nonlinear Fusion Estimation under Resource Constraints
论文作者
论文摘要
本文研究了一类非线性网络多传感器融合系统的事件触发的分布式融合估计问题，没有噪声统计特征。在考虑两种通信渠道的有限资源问题（即传感器到远程估计器频道和智能传感器到融合中心渠道）时，在统一的网络框架中引入了事件触发的策略和降低维度降低策略，以减轻通信负担。然后，根据统一模型的两种补偿策略旨在重组未传输的信息，并根据补偿信息提出了本地/融合估计器。此外，在建立估计误差系统时，由泰勒膨胀引起的线性化误差是由具有不确定参数的状态依赖性矩阵建模的，然后构建了不同的可靠递归优化问题，以确定估计器的收益和融合标准。同时，还提出了稳定性条件，以使设计的非线性估计器的平方误差有界限。最后，采用车辆定位系统来证明所提出方法的有效性和优势。
This paper studies the event-triggered distributed fusion estimation problems for a class of nonlinear networked multisensor fusion systems without noise statistical characteristics. When considering the limited resource problems of two kinds of communication channels (i.e., sensor-to-remote estimator channel and smart sensor-to-fusion center channel), an event-triggered strategy and a dimensionality reduction strategy are introduced in a unified networked framework to lighten the communication burden. Then, two kinds of compensation strategies in terms of a unified model are designed to restructure the untransmitted information, and the local/fusion estimators are proposed based on the compensation information. Furthermore, the linearization errors caused by the Taylor expansion are modeled by the state-dependent matrices with uncertain parameters when establishing estimation error systems, and then different robust recursive optimization problems are constructed to determine the estimator gains and the fusion criteria. Meanwhile, the stability conditions are also proposed such that the square errors of the designed nonlinear estimators are bounded. Finally, a vehicle localization system is employed to demonstrate the effectiveness and advantages of the proposed methods.
