{"title":"Performance of GPS and IMU sensor fusion using unscented Kalman filter for precise i-Boat navigation in infinite wide waters","authors":"Mokhamad Nur Cahyadi , Tahiyatul Asfihani , Ronny Mardiyanto , Risa Erfianti","doi":"10.1016/j.geog.2022.11.005","DOIUrl":null,"url":null,"abstract":"<div><p>The Unmanned Surface Vehicle (USV) navigation system needs an accurate, firm, and reliable performance to avoid obstacles, as well as carry out automatic movements during missions. The Global Positioning System (GPS) is often used in these systems to provide absolute position information. However, the GPS measurements are affected by external conditions such as atmospheric bias and multipath effects. This leads to the inability of the stand-alone GPS to provide accurate positioning for the USV systems. One of the solutions to correct the errors of this sensor is by conducting GPS and Inertial Measurement Unit (IMU) fusion. The IMU sensor is complementary to the GPS and not affected by external conditions. However, it accumulates noise as time elapses. Therefore, this study aims to determine the fusion of the GPS and IMU sensors for the i-Boat navigation system, which is a USV developed by Institut Teknologi Sepuluh Nopember (ITS) Surabaya. Using the Unscented Kalman filter (UKF), sensor fusion was carried out based on the state equation defined by the dynamic and kinematic mathematical model of ship motion in 6 degrees of freedom. Then the performance of this model was tested through several simulations using different combinations of attitude measurement data. Two scenarios were conducted in the simulations: attitude measurement inclusion and exclusion (Scenarios I and II, respectively). The results showed that the position estimation in Scenario II was better than in Scenario I, with the Root Mean Square Error (RMSE) value of 0.062 m. Further simulations showed that the presence of attitude measurement data caused a decrease in the fusion accuracy. The UKF simulation with eight measurement parameters (Scenarios A, B and C) and seven measurement parameters (Scenarios D, E and F), as well as analytical attitude movement, indicated that yaw data had the largest noise accumulation compared to roll and pitch.</p></div>","PeriodicalId":46398,"journal":{"name":"Geodesy and Geodynamics","volume":null,"pages":null},"PeriodicalIF":2.8000,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Geodesy and Geodynamics","FirstCategoryId":"89","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1674984722000969","RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"GEOCHEMISTRY & GEOPHYSICS","Score":null,"Total":0}
引用次数: 3
Abstract
The Unmanned Surface Vehicle (USV) navigation system needs an accurate, firm, and reliable performance to avoid obstacles, as well as carry out automatic movements during missions. The Global Positioning System (GPS) is often used in these systems to provide absolute position information. However, the GPS measurements are affected by external conditions such as atmospheric bias and multipath effects. This leads to the inability of the stand-alone GPS to provide accurate positioning for the USV systems. One of the solutions to correct the errors of this sensor is by conducting GPS and Inertial Measurement Unit (IMU) fusion. The IMU sensor is complementary to the GPS and not affected by external conditions. However, it accumulates noise as time elapses. Therefore, this study aims to determine the fusion of the GPS and IMU sensors for the i-Boat navigation system, which is a USV developed by Institut Teknologi Sepuluh Nopember (ITS) Surabaya. Using the Unscented Kalman filter (UKF), sensor fusion was carried out based on the state equation defined by the dynamic and kinematic mathematical model of ship motion in 6 degrees of freedom. Then the performance of this model was tested through several simulations using different combinations of attitude measurement data. Two scenarios were conducted in the simulations: attitude measurement inclusion and exclusion (Scenarios I and II, respectively). The results showed that the position estimation in Scenario II was better than in Scenario I, with the Root Mean Square Error (RMSE) value of 0.062 m. Further simulations showed that the presence of attitude measurement data caused a decrease in the fusion accuracy. The UKF simulation with eight measurement parameters (Scenarios A, B and C) and seven measurement parameters (Scenarios D, E and F), as well as analytical attitude movement, indicated that yaw data had the largest noise accumulation compared to roll and pitch.
期刊介绍：
Geodesy and Geodynamics launched in October, 2010, and is a bimonthly publication. It is sponsored jointly by Institute of Seismology, China Earthquake Administration, Science Press, and another six agencies. It is an international journal with a Chinese heart. Geodesy and Geodynamics is committed to the publication of quality scientific papers in English in the fields of geodesy and geodynamics from authors around the world. Its aim is to promote a combination between Geodesy and Geodynamics, deepen the application of Geodesy in the field of Geoscience and quicken worldwide fellows'' understanding on scientific research activity in China. It mainly publishes newest research achievements in the field of Geodesy, Geodynamics, Science of Disaster and so on. Aims and Scope: new theories and methods of geodesy; new results of monitoring and studying crustal movement and deformation by using geodetic theories and methods; new ways and achievements in earthquake-prediction investigation by using geodetic theories and methods; new results of crustal movement and deformation studies by using other geologic, hydrological, and geophysical theories and methods; new results of satellite gravity measurements; new development and results of space-to-ground observation technology.
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