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

Abstract

Aiming at the problem of distributed state estimation in sensor networks, a novel optimal distributed finite-time fusion filtering method based on dynamic communication weights has been developed. To tackle the fusion errors caused by incomplete node information in distributed sensor networks, the concept of limited iterations of global information aggregation was introduced, namely, fast finite-time convergence techniques. Firstly, a local filtering algorithm architecture was constructed to achieve fusion error convergence within a limited number of iterations. The maximum number of iterations was derived to be the diameter of the communication topology graph in the sensor network. Based on this, the matrix weight fusion was used to combine the local filtering results, thereby achieving optimal estimation in terms of minimum variance. Next, by introducing the generalized information quality (GIQ) calculation method and associating it with the local fusion result bias, the relative communication weights were obtained and embedded in the fusion algorithm. Finally, the effectiveness and feasibility of the proposed algorithm were validated through numerical simulations and experimental tests.

A Pure Electric Driverless Crawler Construction Machinery Walking Method Based on the Fusion SLAM and Improved Pure Pursuit Algorithms

“…Zong et al [ 16 ] proposed a randomly weighted CKF (RWCKF) to handle the limitations of the CKF, overcoming the CKF problem and improving the accuracy of system state estimation. Yu et al [ 17 ] proposed a distributed finite-time optimal fusion filtering method based on dynamic communication weights for distributed state estimation in sensor networks which achieves fusion error convergence and optimal estimation with minimal variance within a limited number of iterations.…”

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

“…Scholars have conducted in-depth research on multi-sensor distributed fusion strategies. For example, Yu [ 8 ] suggested a method for distributed fusion filtering based on dynamic communication weights to tackle the issue of fusion errors in incomplete node information within distributed sensor networks. The proposed method aims to improve the situation by optimizing the fusion process in a finite time manner.…”