Distributed State Fusion Estimation of Multi-Source Localization Nonlinear Systems

Abstract

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.

Optimal Distributed Finite-Time Fusion Method for Multi-Sensor Networks under Dynamic Communication Weight

“…• First, in contrast to references [12][13][14][15]18,[26][27][28][29], which only consider fusion accuracy or the consistency of the fusion process without achieving rapid convergence of fusion errors and optimal estimation, this paper introduces fast finite-time convergence techniques and matrix weight fusion techniques. It achieves finite-time convergence of fusion errors (the maximum convergence iterations being the graph diameter) and optimal estimation in terms of minimum variance.…”

An Efficient Implementation Method for Distributed Fusion in Sensor Networks Based on CPHD Filters

“…Senel [ 9 ] conducted research and analysis in the fields of multi-sensor data fusion and real-time multi-target tracking. Liu [ 10 ] proposed a distributed sequential ellipsoidal intersection fusion estimation algorithm. This algorithm combines the local filter estimates obtained at the fusion center in order to achieve more precise fusion localization results.…”