Cooperative Terrain Navigation Using Hybrid GMM/SMC Message Passing on Factor Graphs

被引：8

作者：

Oliveira, Hallysson ^{[1
]}

Dias, Stiven Schwanz ^{[1
]}

Bruno, Marcelo Gomes da Silva ^{[2
]}

机构：

[1] Embraer SA, BR-12247820 Sao Jose Dos Campos, SP, Brazil

[2] Inst Tecnol Aeronaut, BR-12228900 Sao Jose Dos Campos, SP, Brazil

来源：

IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS | 2020年 / 56卷 / 05期

基金：

巴西圣保罗研究基金会;

关键词：

Aircraft navigation; Aircraft; Sum product algorithm; Random variables; Monte Carlo methods; Message passing; Gaussian mixture model; Cooperative localization; distributed Bayesian estimation; factor graphs; Gaussian mixture models; sequential Monte Carlo; sum-product algorithm; terrain aided navigation; LOCALIZATION; TRACKING; FILTER;

D O I：

10.1109/TAES.2020.2985316

中图分类号：

V [航空、航天];

学科分类号：

08 ; 0825 ;

摘要：

We introduce in this article a distributed factor-graph-based algorithm for anchorless cooperative aircraft localization in a GNSS-denied scenario without fixed infrastructure. The agents use terrain-aided navigation (TAN) to perform local position estimation and exchange messages to improve their position beliefs. Internode communication cost is reduced using a hybrid Gaussian mixture model / sequential Monte Carlo (GMM/SMC) approach. Simulation results show that, even in a partially connected network where only a small part of the agents perform TAN, cooperation yields better results than all aircraft performing TAN independently.

引用

页码：3958 / 3970

页数：13

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