The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
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The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
As informações de posicionamento desempenham um papel significativo em aplicações de veículos aéreos não tripulados (UAVs). Tradicionalmente, as informações de posicionamento são amplamente fornecidas pelo Sistema Global de Navegação por Satélite (GNSS) devido ao seu bom desempenho e cobertura global. No entanto, devido ao ambiente de voo complicado ou ao bloqueio do sinal, interferências e interferências não intencionais, os UAV podem não conseguir localizar-se utilizando apenas o GNSS. Como um novo método para resolver estes problemas, o posicionamento cooperativo, ao incorporar medições de alcance peer-to-peer e informações assistidas, tem atraído cada vez mais atenções devido à sua capacidade de aumentar a precisão e a disponibilidade do posicionamento. No entanto, alcançar um bom desempenho de posicionamento cooperativo de multi-UAVs é um desafio devido à sua mobilidade, evolução de estado não linear arbitrária, modelos de medição e recursos limitados de computação e comunicação. Neste artigo, apresentamos uma metodologia de representação de gráfico fatorial (FG) e passagem de mensagens para resolver problemas de posicionamento cooperativo entre UAVs em ambiente tridimensional onde o GNSS não pode fornecer serviços. Além disso, para lidar com os modelos de medição e evolução de estado não linear e, ao mesmo tempo, diminuir a complexidade computacional e o custo de comunicação, desenvolvemos um algoritmo distribuído para UAVs dinâmicos e híbridos por meio do método de Regras de Cubatura Esférica-Radial (CR) com propagação de crenças (BP) e métodos variacionais de passagem de mensagens (VMP) (CRBP-VMP) no FG. O CRBP proposto lida com modelos de evolução de estado altamente não lineares e distribuições não gaussianas, o método VMP é empregado para alcance de mensagens, obtém uma representação de mensagens mais simples e pode reduzir o custo de comunicação no problema de estimativa conjunta. Os resultados da simulação demonstram que a maior precisão de posicionamento, a melhor convergência, bem como a baixa complexidade computacional e custo de comunicação do algoritmo CRBP-VMP proposto, que pode ser alcançado em comparação com o algoritmo de produto de soma em uma rede sem fio (SPAWN) e o tradicional Cubature Kalman Método de filtros (CKF).
Lu LU
Army Engineering University,Test and Assessment Research Center of China Satellite Navigation Office (TARC-CSNO)
Guangxia LI
Army Engineering University
Tianwei LIU
Army Engineering University
Siming LI
Army Engineering University
Shiwei TIAN
Army Engineering University
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Lu LU, Guangxia LI, Tianwei LIU, Siming LI, Shiwei TIAN, "A Hybrid CRBP-VMP Cooperative Positioning Algorithm for Distributed Multi-UAVs" in IEICE TRANSACTIONS on Communications,
vol. E102-B, no. 10, pp. 1933-1940, October 2019, doi: 10.1587/transcom.2018DRP0004.
Abstract: Positioning information plays a significant role in multi-unmanned aerial vehicles (UAVs) applications. Traditionally, the positioning information is widely provided by Global Navigation Satellite System (GNSS) due to its good performance and global coverage. However, owing to complicated flight environment or signal blockage, jamming and unintentional interference, the UAVs may fail to locate themselves by using GNSS alone. As a new method to resolve these problems, cooperative positioning, by incorporating peer-to-peer range measurements and assisted information, has attracted more and more attentions due to its ability to enhance the accuracy and availability of positioning. However, achieving good performance of cooperative positioning of multi-UAVs is challenging as their mobility, arbitrary nonlinear state-evolution, measurement models and limited computation and communication resources. In this paper, we present a factor graph (FG) representation and message passing methodology to solve cooperative positioning problem among UAVs in 3-dimensional environment where GNSS cannot provide services. Moreover, to deal with the nonlinear state-evolution and measurement models while decreasing the computation complexity and communication cost, we develop a distributed algorithm for dynamic and hybrid UAVs by means of Spherical-Radial Cubature Rules (CR) method with belief propagation (BP) and variational message passing (VMP) methods (CRBP-VMP) on the FG. The proposed CRBP deals with the highly non-linear state-evolution models and non-Gaussian distributions, the VMP method is employed for ranging message, gets the simpler message representation and can reduce communication cost in the joint estimation problem. Simulation results demonstrate that the higher positioning accuracy, the better convergence as well as low computational complexity and communication cost of the proposed CRBP-VMP algorithm, which can be achieved compared with sum-product algorithm over a wireless network (SPAWN) and traditional Cubature Kalman Filters (CKF) method.
URL: https://global.ieice.org/en_transactions/communications/10.1587/transcom.2018DRP0004/_p
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@ARTICLE{e102-b_10_1933,
author={Lu LU, Guangxia LI, Tianwei LIU, Siming LI, Shiwei TIAN, },
journal={IEICE TRANSACTIONS on Communications},
title={A Hybrid CRBP-VMP Cooperative Positioning Algorithm for Distributed Multi-UAVs},
year={2019},
volume={E102-B},
number={10},
pages={1933-1940},
abstract={Positioning information plays a significant role in multi-unmanned aerial vehicles (UAVs) applications. Traditionally, the positioning information is widely provided by Global Navigation Satellite System (GNSS) due to its good performance and global coverage. However, owing to complicated flight environment or signal blockage, jamming and unintentional interference, the UAVs may fail to locate themselves by using GNSS alone. As a new method to resolve these problems, cooperative positioning, by incorporating peer-to-peer range measurements and assisted information, has attracted more and more attentions due to its ability to enhance the accuracy and availability of positioning. However, achieving good performance of cooperative positioning of multi-UAVs is challenging as their mobility, arbitrary nonlinear state-evolution, measurement models and limited computation and communication resources. In this paper, we present a factor graph (FG) representation and message passing methodology to solve cooperative positioning problem among UAVs in 3-dimensional environment where GNSS cannot provide services. Moreover, to deal with the nonlinear state-evolution and measurement models while decreasing the computation complexity and communication cost, we develop a distributed algorithm for dynamic and hybrid UAVs by means of Spherical-Radial Cubature Rules (CR) method with belief propagation (BP) and variational message passing (VMP) methods (CRBP-VMP) on the FG. The proposed CRBP deals with the highly non-linear state-evolution models and non-Gaussian distributions, the VMP method is employed for ranging message, gets the simpler message representation and can reduce communication cost in the joint estimation problem. Simulation results demonstrate that the higher positioning accuracy, the better convergence as well as low computational complexity and communication cost of the proposed CRBP-VMP algorithm, which can be achieved compared with sum-product algorithm over a wireless network (SPAWN) and traditional Cubature Kalman Filters (CKF) method.},
keywords={},
doi={10.1587/transcom.2018DRP0004},
ISSN={1745-1345},
month={October},}
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TY - JOUR
TI - A Hybrid CRBP-VMP Cooperative Positioning Algorithm for Distributed Multi-UAVs
T2 - IEICE TRANSACTIONS on Communications
SP - 1933
EP - 1940
AU - Lu LU
AU - Guangxia LI
AU - Tianwei LIU
AU - Siming LI
AU - Shiwei TIAN
PY - 2019
DO - 10.1587/transcom.2018DRP0004
JO - IEICE TRANSACTIONS on Communications
SN - 1745-1345
VL - E102-B
IS - 10
JA - IEICE TRANSACTIONS on Communications
Y1 - October 2019
AB - Positioning information plays a significant role in multi-unmanned aerial vehicles (UAVs) applications. Traditionally, the positioning information is widely provided by Global Navigation Satellite System (GNSS) due to its good performance and global coverage. However, owing to complicated flight environment or signal blockage, jamming and unintentional interference, the UAVs may fail to locate themselves by using GNSS alone. As a new method to resolve these problems, cooperative positioning, by incorporating peer-to-peer range measurements and assisted information, has attracted more and more attentions due to its ability to enhance the accuracy and availability of positioning. However, achieving good performance of cooperative positioning of multi-UAVs is challenging as their mobility, arbitrary nonlinear state-evolution, measurement models and limited computation and communication resources. In this paper, we present a factor graph (FG) representation and message passing methodology to solve cooperative positioning problem among UAVs in 3-dimensional environment where GNSS cannot provide services. Moreover, to deal with the nonlinear state-evolution and measurement models while decreasing the computation complexity and communication cost, we develop a distributed algorithm for dynamic and hybrid UAVs by means of Spherical-Radial Cubature Rules (CR) method with belief propagation (BP) and variational message passing (VMP) methods (CRBP-VMP) on the FG. The proposed CRBP deals with the highly non-linear state-evolution models and non-Gaussian distributions, the VMP method is employed for ranging message, gets the simpler message representation and can reduce communication cost in the joint estimation problem. Simulation results demonstrate that the higher positioning accuracy, the better convergence as well as low computational complexity and communication cost of the proposed CRBP-VMP algorithm, which can be achieved compared with sum-product algorithm over a wireless network (SPAWN) and traditional Cubature Kalman Filters (CKF) method.
ER -