Integration of GNSS-IMU for increasing the observation accuracy in Condensed Areas (Infrastructure and Forest Canopies)

Cahyadi, Mokhamad Nur; Rwabudandi, Irene

doi:10.1051/e3sconf/20199403015

Cited by 7 publications

(6 citation statements)

References 4 publications

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“…The result of this activity is in the form of travel point data that is recorded by each receiver with an observation interval of 1 second. One of the receivers used is already integrated with IMU/INS which has the benefit of reducing movement noise in GNSS data [4]. The table above shows the results of the comparison of the average position values of the low-cost GNSS receiver while moving.…”

Section: Experiments 2 (Kinematic Survey and Imu/ins)mentioning

confidence: 99%

Low Cost GNSS Trimble BD982 and U-blox Performance Test Analysis F9 Series for Several Measurement Methods (Case Study: Sidoarjo Regency)

Ubaidillah¹,

Cahyadi²

2022

IOP Conf. Ser.: Earth Environ. Sci.

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Global Navigation Satellite System (GNSS) is a tool that can assist community activities, especially in navigation and positioning for mapping surveys. This tool has an accuracy of up to millimeters for determining the position of latitude and longitude, but to get this accuracy requires an expensive cost, which is more than 200 million rupiah. Currently the development of GNSS receivers is very rapid, one of which is a low-cost GNSS product. The advantages of this receiver are that it has a low cost, light weight and an accuracy of up to centimeters for determining latitude and longitude positions. In this study, we have conducted trials using a low-cost GNSS receiver from several products such as U-blox, Trimble, and Comnav. For products from U-blox in this study, the F9P and F9R series were used. The F9R series is one of the receivers that has been integrated with the inertial measurement unit (IMU)/inertial navigation system (INS), the benefit of this integration is to improve position accuracy when data collection using the kinematic method. Products from U-blox have position accuracy below 1 meter. Meanwhile, the series used in Trimble and Comnav products are Trimble BD982 and Comnav Oem Board K708 which have accuracy below 1 centimeter. The purpose of this activity is to compare all these receivers by using Comnav OEM Board K708 data as validation data. Several parameters in this study will be analyzed such as the number of satellites obtained by the receiver, position accuracy and various other analyzes. The measurement methods that will be carried out in this study are static differential and Real Time Kinematic (RTK) measurements that utilize direct corrections from Continuously Operating Reference Stations (CORS) and deformation observations. The results of the first analysis obtained in this activity are the number of satellites obtained by the U-blox F9 series receiver which is more stable to receive satellites, namely as many as 24-29 satellites, then Trimble BD982 as many as 2-32 satellites, and Comnav K708 receivers as many as 3-28 satellites. Meanwhile, the Dilution Of Precision (DOP) values for 4 receivers, namely K708, F9P, and F9R, have stable values ranging from 0.5-1 compared to the BD982 receiver which fluctuated in several observation epochs, especially in the last observation epoch. In observations using the static differential method, using corrections from CORS for positioning this receiver can be used because the difference in position data is in the range of 0.009 m - 0.040 m from reference data (receiver K708). Meanwhile, the data receiver that has been integrated with IMU/INS (F9R) kinematic method has the smallest position difference value (K708) compared to other receivers, which is from 0.001 m - 8.091 m. In testing for deformation measurements, further research needs to be done because the data obtained is still in the form of a simulation.

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Section: Experiments 2 (Kinematic Survey and Imu/ins)mentioning

confidence: 99%

Low Cost GNSS Trimble BD982 and U-blox Performance Test Analysis F9 Series for Several Measurement Methods (Case Study: Sidoarjo Regency)

Ubaidillah¹,

Cahyadi²

2022

IOP Conf. Ser.: Earth Environ. Sci.

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“…The "first order" approximations to the optimal terms are provided by the linearization technique. These approximations in the mean and covariance of the state estimation produce second order errors, and filter divergence might occur as a result (Cahyadi and Rwabudandi, 2019). Then, the EKF equations are used after the linear model has been generated.…”

Section: Equationmentioning

confidence: 99%

“…The main contributions of this paper lies in the following aspects: (1) The processing the data generated from the recording by the water surface vehicle using the EKF and smoothing algorithm, (2) The results of the integration of GNSS and IMU using EKF are compared with the fusion results using the UKF script, (3) The accuracy of the fusion results is analyzed based on the variables of position, linear velocity, and attitude. EKF and smoothing algorithm was chosen because in the research by Cahyadi and Rwabudandi (2019) it was proven to be able to produce more accurate and smoother position than the use of stand-alone GNSS due to the smoothing algorithm.…”

Section: Introductionmentioning

confidence: 99%

Loosely Coupled GNSS and IMU Integration for Accurate i-Boat Horizontal Navigation

2022

IJG

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Global Navigation Satellite System (GNSS) has been widely utilized as a navigation solution for a mobile vehicle, yet stand-alone GNSS is unable to achieve sufficient accuracy in some applications. For example, in aquatic environment, the accuracy position of Unmanned Surface Vehicle (USV) is affected by the wind, current, and waves dynamics. In this case, the integration of GNSS and Inertial Measurement Unit (IMU) is an approach that can be used to support USV localization to achieve an accurate navigation solution. This study integrates GNSS and IMU using Extended Kalman Filter (EKF) to process loosely coupled integration. The integration results show that the estimated x-position is 0.3058 m accurately and the y-position has an accuracy of 0.2780 m. Then, the loosely coupled integration results of EKF were compared with Unscented Kalman Filter (UKF) simulation in the previous studies. The integration of GNSS and IMU using EKF produces a higher RMSE value of 2D position and attitude angle than UKF Scenario I. However, due to the smoothing process, EKF can provide a visually smoother trajectory than UKF, although it has a significant difference from the observed trajectory. On the other hand, the linear velocity estimated by EKF shows better stability compared to UKF in both Scenario I and II.

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“…The best accuracy of 7.13 m was obtained when the integration involved the regional navigational satellite system, NavIC (Navigation with Indian Constellation). Nagui et al [16], through their research, also proved that using loosely coupled integration for land vehicle navigation by filtering the IMU and GNSS readings can produce 3D position accuracy of 0.68 m. An increment in the accuracy results of loosely coupled integration in condensed areas and urban areas is also obtained by performing post-processing smoothing [18][19][20]. Among all these studies, the Extended Kalman Filter (EKF) algorithm is used to perform loosely coupled integration.…”

Section: Introductionmentioning

confidence: 99%

GNSS/IMU Sensor Fusion Performance Comparison of a Car Localization in Urban Environment Using Extended Kalman Filter

Erfianti

Asfihani

Suhandri

2023

IOP Conf. Ser.: Earth Environ. Sci.

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Global Navigation Satellite System (GNSS) and Inertial Measurement Unit (IMU) are popular navigation sensor for position fixing technique and dead reckoning system that complement each other. GNSS can provide accurate position and velocity information when it establishes a Line of Sight (LOS) with a minimum of four satellites. However, this accuracy can decrease due to signal outage, jamming, interference, and multipath effects. On the other hand, the IMU has the advantage of measuring the platform’s orientation with a high-frequency update and is not affected by environmental conditions. However, a drift effect causes the measurement errors to accumulate. Several studies have demonstrated the fusion of both sensors in terms of the Extended Kalman Filter (EKF). This study conduct sensor fusion for car localization in an urban environment based on the loosely coupled integration scheme. In order to improve the sensor fusion performance, pre-processing GNSS and IMU data were applied. The result shows that pre-processing DGNSS and IMU filtering can increase the accuracy of the integrated navigation solution up to 80.02% in the east, 80.13% in the north, and 89.45% in the up direction during the free outage period.

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Integration of GNSS-IMU for increasing the observation accuracy in Condensed Areas (Infrastructure and Forest Canopies)

Cited by 7 publications

References 4 publications

Low Cost GNSS Trimble BD982 and U-blox Performance Test Analysis F9 Series for Several Measurement Methods (Case Study: Sidoarjo Regency)

Low Cost GNSS Trimble BD982 and U-blox Performance Test Analysis F9 Series for Several Measurement Methods (Case Study: Sidoarjo Regency)

Loosely Coupled GNSS and IMU Integration for Accurate i-Boat Horizontal Navigation

GNSS/IMU Sensor Fusion Performance Comparison of a Car Localization in Urban Environment Using Extended Kalman Filter

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