Academy for Advanced Interdisciplinary Research, North University of China, Taiyuan 030051, China.
School of Instrument and Intelligence, North University of China, Taiyuan 030051, China.
Sensors (Basel). 2023 Jan 7;23(2):698. doi: 10.3390/s23020698.
For the state estimation problem of a multi-source localization nonlinear system with unknown and bounded noise, a distributed sequential ellipsoidal intersection fusion estimation algorithm based on the dual set-membership filtering method is proposed to ensure the reliability of the localization system. First, noise with unknown and bounded characteristics is modeled by using bounded ellipsoidal regions. At the same time, local estimators are designed at the sensor link nodes to filter out the noise interference in the localization system. The local estimator is designed using the dual set-membership filtering algorithm. It uses the dual principle to find the minimizing ellipsoid that can contain the nonlinear function by solving the optimization problem with semi-infinite constraints, and a first-order conditional gradient algorithm is used to solve the optimization problem with a low computational complexity. Meanwhile, the communication confusion among multiple sensors causes the problem of unknown correlation. The obtained estimates of local filters are fused at the fusion center by designing a distributed sequential ellipsoid intersection fusion estimation algorithm to obtain more accurate fusion localization results with lower computational cost. Finally, the stability and reliability of the proposed distributed fusion algorithm are verified by designing a simulation example of a multi-source nonlinear system.
针对具有未知有界噪声的多源定位非线性系统的状态估计问题，提出了一种基于对偶集成员滤波方法的分布式序椭球交集融合估计算法，以确保定位系统的可靠性。首先，通过使用有界椭球区域对具有未知有界特性的噪声进行建模。同时，在传感器链路节点处设计局部估计器，以滤除定位系统中的噪声干扰。局部估计器使用对偶集成员滤波算法设计。它使用对偶原理通过求解具有半无限约束的优化问题找到可以包含非线性函数的最小椭球，并使用一阶条件梯度算法求解具有低计算复杂度的优化问题。同时，多个传感器之间的通信混淆导致了未知相关性的问题。通过设计分布式序椭球交集融合估计算法，在融合中心对多个局部滤波器的估计值进行融合，以获得具有更低计算成本的更准确的融合定位结果。最后，通过设计多源非线性系统的仿真示例验证了所提出的分布式融合算法的稳定性和可靠性。
