Geographical Information Enhanced Cooperative Localization in Vehicular Ad-Hoc Networks

Luo, Feng; Wang, Shengchu; Gong, Yi; Jing, Xiaojun; Zhang, Lin

doi:10.1109/lsp.2018.2806374

Cited by 22 publications

(10 citation statements)

References 17 publications

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“…For the faulty cases caused by NLOS or multipath, such faulty measurements can be removed by some integrity methods. 34 The observation function is…”

Section: Hybrid Cooperative Navigationmentioning

confidence: 99%

A hybrid cooperative navigation method for UAV swarm based on factor graph and Kalman filter

Chen

Xiong

2022

International Journal of Distributed Sensor Networks

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Navigation plays an important role in the task execution of the micro-unmanned aerial vehicle (UAV) swarm. The Cooperative Navigation (CN) method that fuses the observation of onboard sensors and relative information between UAVs is a research hotspot. Aiming at the efficiency and accuracy problems of previous studies, this article proposes a hybrid-CN method for UAV swarm based on Factor Graph and Kalman filter. A global Factor Graph is used to combine Global Navigation Satellite System (GNSS) and ranging information to provide position estimations for modifying the distributed Kalman filter; distributed Kalman filter is established on each UAV to fuse inertial information and optimized position estimation to modify the navigation states. In order to provide time-consistent GNSS position information for the Factor Graph, a time synchronization filter is designed. The proposed method is tested and verified using standard Monte Carlo simulations, simulation results show that it can provide a more precise and efficient CN solution than traditional CN methods.

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“…For the faulty cases caused by NLOS or multipath, such faulty measurements can be removed by some integrity methods. 34 The observation function is…”

Section: Hybrid Cooperative Navigationmentioning

confidence: 99%

A hybrid cooperative navigation method for UAV swarm based on factor graph and Kalman filter

Chen

Xiong

2022

International Journal of Distributed Sensor Networks

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“…Inversely, the user-based is more suitable than BS for vehicle networks. Besides, cooperative vehicle localization plays an effective model for the user-based [9][10][11]. In general, non-Bayesian-based and Bayesian-based are considered for cooperative localization.…”

Section: Introductionmentioning

confidence: 99%

Evolutionary Coalitional Game Based Cooperative Localization in Vehicular Network

Yin¹,

Zou²,

Lu³

et al. 2022

Preprint

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Cooperative localization under complex urban environments has become a solution to replace the Global Navigation Satellite System (GNSS) positioning. Due to utilizing distance measured and information exchanged between vehicles, cooperative localization results in high computational complexity and heavy communication overhead. This paper proposes a cooperative localization method based on evolutionary coalitional game theory, which implements vehicles' location estimation with less communication cost. We select the neighboring vehicles to form a coalition based on the node's square position error bound and communication cost. The location is obtained via exchanging information between vehicles. It is evident from the simulations and results that the proposed method holds low communication overhead while maintaining localization accuracy.

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“…8 The aforementioned challenges emphasize the necessity of conducting continuous research for identifying the number of obstacles that hinder the transmission of emergency messages. 9 The obstacles should be determined for ultimately minimizing the number of collisions with a view to ensure timely message reception at the target vehicles and rapid decision process by the drivers of the source vehicle for reactive response in stimulus to the emergency event. 10 Moreover, the vehicular nodes with different dimensional speeds and compositions can contribute to the additional Non-Line-of-Sight (NLOS) situations.…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, foliage, tall buildings and other vehicles can also act as obstacles in the process of transmitting warning message from emergency vehicles to the target police or ambulance vehicles 8 . The aforementioned challenges emphasize the necessity of conducting continuous research for identifying the number of obstacles that hinder the transmission of emergency messages 9 . The obstacles should be determined for ultimately minimizing the number of collisions with a view to ensure timely message reception at the target vehicles and rapid decision process by the drivers of the source vehicle for reactive response in stimulus to the emergency event 10 .…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Crow Search and Gray Wolf Optimization Algorithm‐based Reliable Non‐Line‐of‐Sight Node Positioning Scheme for Vehicular Ad hoc Networks

Malar¹,

Priya²,

Janakiraman³

2020

Int J Communication

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Summary Vehicular Ad hoc NETwork (VANET) facilitates ubiquitous connectivity for establishing Vehicle‐to‐Vehicle (V2V) communication and supporting Intelligent Transportation Systems (ITSs). This vehicle communication requires complete coverage within the target range for ensuring reliable message dissemination. High density of vehicles in the intersections introduces challenges due to obstacles such as buildings, foliage, and other moving vehicles, preventing exchange of information about location and message update between vehicles. Non‐Line‐of‐Sight (NLOS) nodes also introduce broadcasting storm problem leading to congestion that prevents emergency messages from reaching the target vehicular nodes. Integration of meta‐heuristic Crow Search Algorithm (CSA) and Gray Wolf Optimization Algorithm (GWOA) minimizes the objective function of NLOS localization problem without the solution being trapped into local optima. In this paper, a Hybrid Crow Search and GWOA‐based NLOS Positioning Scheme (HCSGWOA‐NLOS‐PS) is proposed to handle the issue of broadcast storm and facilitate reliability in emergency message delivery. The proposed HCSGWOA‐NLOS‐PS utilizes the benefits of Time of Arrival (ToA) and geographical information‐based cooperative localization agent for attaining efficient NLOS node positioning in the network. It uses the benefits of CSA and GWOA for positioning the NLOS nodes based on the intelligence derived from the crows' conduit and the social attacking behavior of gray wolves that are ideal for balancing the tradeoff between exploration and exploitation. The simulation results of the proposed HCSGWOA‐NLOS‐PS confirm a mean emergency message delivery of 11.82%, neighborhood awareness of 12.38% with reduced localization error rate of 2.36% and minimized delay of 8.64% when compared to the baseline approaches.

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Geographical Information Enhanced Cooperative Localization in Vehicular Ad-Hoc Networks

Cited by 22 publications

References 17 publications

A hybrid cooperative navigation method for UAV swarm based on factor graph and Kalman filter

A hybrid cooperative navigation method for UAV swarm based on factor graph and Kalman filter

Evolutionary Coalitional Game Based Cooperative Localization in Vehicular Network

A Hybrid Crow Search and Gray Wolf Optimization Algorithm‐based Reliable Non‐Line‐of‐Sight Node Positioning Scheme for Vehicular Ad hoc Networks

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