Multi-Class Hierarchical Random Networks for Consensus-Based Information Filter

Yuqi Wang

doi:10.1109/TCSI.2024.3516371

Multi-Class Hierarchical Random Networks for Consensus-Based Information Filter

2024-01-01

0

PRE

AI

DOI：10.1109/TCSI.2024.3516371

原文链接

原文求助

摘要

En 中文

This study proposes a general multi-class hierarchical random network with an arbitrary number of agents and an arbitrary connecting success probability to analyze the minimum communication costs with the minimum consensus iterations in distributed multi-agent networks. The proposed hierarchical random networks facilitate a flexible network topology that is well-suited for implementing consensus filters and verifying network connectivity. Further, the study validates the connectivity conditions, determines the upper bound of diameters, and calculates the minimum number of expected network connections for the proposed multi-class hierarchical random network. Total network connections, communication costs, and root mean square errors of consensus fused estimations in the proposed multi-class hierarchical networks are compared via a 2-D target tracking scenario. The simulation result demonstrates that the proposed multi-class hierarchical consensus filter can achieve maneuvering target tracking while avoiding the polynomial increment of communication costs.

Keyword:

Costs

Network topology

Information filters

Target tracking

Upper bound

Topology

Probability density function

State estimation

Probability distribution

Polynomials

communication cost

random network

hierarchical network

consensus filter

Multi-Class Hierarchical Random Networks for Consensus-Based Information Filter

摘要

期刊

机构

引用论文