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
收藏
页码：3958 / 3970
页数：13
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