Performance Analysis of Relative GPS Positioning as Function of Communication Latency

Carvalho, Gustavo S.; Silva, Felipe O.

doi:10.1109/lars/sbr/wre54079.2021.9605477

2021 Latin American Robotics Symposium (LARS), 2021 Brazilian Symposium on Robotics (SBR), and 2021 Workshop on Robotics in Edu 2021

DOI: 10.1109/lars/sbr/wre54079.2021.9605477

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Performance Analysis of Relative GPS Positioning as Function of Communication Latency

Gustavo S. Carvalho

Felipe O. Silva

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2023

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Cited by 2 publications

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Attention Mechanism-Based Bidirectional Long Short-Term Memory for Cycling Activity Recognition Using Smartphones

Nguyen,

Kim,

Suh

2023

IEEE Access

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Bicycles are an ecofriendly mode of transportation, and cycling offers physical and mental well-being. However, their increased use has resulted in frequent bicycle-human accidents, car-to-bicycle collisions, related injuries and cyclist crashes. Moreover, rules for safe cycling are limited. Smart healthcare systems using smartphones and/or wearable devices, such as a cycling monitoring application that can inform fellow cyclists about the state of the user, can be developed to provide assistance during such unexpected events. In this study, a one-dimensional convolutional neural network (1DCNN)-bidirectional long shortterm memory (BiLSTM) based on attention mechanism (CBiAM) model is proposed for detecting cyclists' states using a mobile phone, thereby enhancing their safety and promoting a secure cycling experience in case of accidents or emergencies. In addition, the "cycling safe (CySa) dataset," a new dataset containing data on the cyclists' actions during cycling, collected from a smartphone positioned in the cyclists' pocket is presented. The proposed CBiAM model was trained on the CySa dataset using different sliding window sizes, batch sizes (Bz), and learning rates (Lr). Experimental results confirmed the superior performance of the proposed model compared to conventional approaches, such as support vector machines and artificial neural networks, and existing advanced architectures, such as 1DCNN, long short-term memory (LSTM), and Bi-LSTM. The robustness of the model was validated using public datasets, such as UCI-human activity recognition (HAR), PAMAP2, Opportunity, MOTIONSENSE, and WISDM, where it achieved impressive F1-scores of 97.51%, 99.82%, 94.72%, 97.67%, and 87.05%, respectively.

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Attention Mechanism-Based Bidirectional Long Short-Term Memory for Cycling Activity Recognition Using Smartphones

Nguyen,

Kim,

Suh

2023

IEEE Access

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Performance Analysis of Relative GPS Positioning for Low-Cost Receiver-Equipped Agricultural Rovers

Carvalho,

Silva,

Pacheco

et al. 2023

Sensors

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Global navigation satellite systems (GNSSs) became an integral part of all aspects of our lives, whether for positioning, navigation, or timing services. These systems are central to a range of applications including road, aviation, maritime, and location-based services, agriculture, and surveying. The Global Positioning System (GPS) Standard Position Service (SPS) provides position accuracy up to 10 m. However, some modern-day applications, such as precision agriculture (PA), smart farms, and Agriculture 4.0, have demanded navigation technologies able to provide more accurate positioning at a low cost, especially for vehicle guidance and variable rate technology purposes. The Society of Automotive Engineers (SAE), for instance, through its standard J2945 defines a maximum of 1.5 m of horizontal positioning error at 68% probability (1σ), aiming at terrestrial vehicle-to-vehicle (V2V) applications. GPS position accuracy may be improved by addressing the common-mode errors contained in its observables, and relative GNSS (RGNSS) is a well-known technique for overcoming this issue. This paper builds upon previous research conducted by the authors and investigates the sensitivity of the position estimation accuracy of low-cost receiver-equipped agricultural rovers as a function of two degradation factors that RGNSS is susceptible to: communication failures and baseline distances between GPS receivers. The extended Kalman filter (EKF) approach is used for position estimation, based on which we show that it is possible to achieve 1.5 m horizontal accuracy at 68% probability (1σ) for communication failures up to 3000 s and baseline separation of around 1500 km. Experimental data from the Brazilian Network for Continuous Monitoring of GNSS (RBMC) and a moving agricultural rover equipped with a low-cost GPS receiver are used to validate the analysis.

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Performance Analysis of Relative GPS Positioning as Function of Communication Latency

Cited by 2 publications

References 13 publications

Attention Mechanism-Based Bidirectional Long Short-Term Memory for Cycling Activity Recognition Using Smartphones

Attention Mechanism-Based Bidirectional Long Short-Term Memory for Cycling Activity Recognition Using Smartphones

Performance Analysis of Relative GPS Positioning for Low-Cost Receiver-Equipped Agricultural Rovers

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