Neural-Kalman GNSS/INS Navigation for Precision Agriculture

Du, Yayun; Saha, Swapnil Sayan; Sandha, Sandeep Singh; Lovekin, Arthur; Wu, Jason; Siddharth, S.; Chowdhary, Mahesh; Jawed, Mohammad Khalid; Srivastava, Mani

doi:10.1109/icra48891.2023.10161351

2023 IEEE International Conference on Robotics and Automation (ICRA) 2023

DOI: 10.1109/icra48891.2023.10161351

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Neural-Kalman GNSS/INS Navigation for Precision Agriculture

Yayun Du,

Swapnil Sayan Saha,

Sandeep Singh Sandha

et al.

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Cited by 2 publications

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Random forest-based multipath parameter estimation

Qi,

Xu,

Wang

et al. 2024

GPS Solut

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Multipath is recognized as one of the major error sources for GNSS urban navigation. This study proposes a random forest (RF)-based multipath parameter estimator that uses random forest regression for parameter estimation, thereby mitigating multipath effect by removing the estimated reflected signal components. The proposed estimator is evaluated and compared with the multipath estimation delay-lock loop (MEDLL) for one-multipath and three-multipath cases, respectively. Simulation results demonstrate that the RF-based estimator is less affected by the front-end bandwidth of received signals, compared with MEDLL. The proposed RF-based estimator shows better performance than MEDLL for signals with front-end bandwidths of lower than 6 MHz. In 20 sets of tests on signals with a front-end bandwidth of 10 MHz in the three-multipath case, the RF-based estimator obtains smaller standard deviations than MEDLL. In experiments using real data with a front-end bandwidth of 2 MHz, the RF-based estimator reduces the 2D and 3D positioning errors by 8.5% and 8.7% over 180 epochs, respectively, against the conventional delayed-locked loop (DLL).

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Random forest-based multipath parameter estimation

Qi,

Xu,

Wang

et al. 2024

GPS Solut

View full text Add to dashboard Cite

show abstract

The Path to Smart Farming: Innovations and Opportunities in Precision Agriculture

Karunathilake,

Le,

Heo

et al. 2023

Agriculture

118

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Precision agriculture employs cutting-edge technologies to increase agricultural productivity while reducing adverse impacts on the environment. Precision agriculture is a farming approach that uses advanced technology and data analysis to maximize crop yields, cut waste, and increase productivity. It is a potential strategy for tackling some of the major issues confronting contemporary agriculture, such as feeding a growing world population while reducing environmental effects. This review article examines some of the latest recent advances in precision agriculture, including the Internet of Things (IoT) and how to make use of big data. This review article aims to provide an overview of the recent innovations, challenges, and future prospects of precision agriculture and smart farming. It presents an analysis of the current state of precision agriculture, including the most recent innovations in technology, such as drones, sensors, and machine learning. The article also discusses some of the main challenges faced by precision agriculture, including data management, technology adoption, and cost-effectiveness.

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Neural-Kalman GNSS/INS Navigation for Precision Agriculture

Cited by 2 publications

References 68 publications

Random forest-based multipath parameter estimation

Random forest-based multipath parameter estimation

The Path to Smart Farming: Innovations and Opportunities in Precision Agriculture

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