Method to improve accuracy of positioning object by eLoran system with applying standard Kalman filter

Grunin, A. P.; Kalinov, G. A.; Bolokhovtsev, A. V.; Sai, S. V.

doi:10.1088/1742-6596/1015/3/032050

Cited by 10 publications

(6 citation statements)

References 6 publications

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“…In addition to its application in geospatial analysis, kriging interpolation has been utilized in numerical simulations, multivariate function estimation, and structured forecasting. This geostatistical method leverages spatial analysis of variance functions to estimate unbiased optimal values of regionalized variables within a finite area [7,8].…”

Section: Kriging Interpolationmentioning

confidence: 99%

Study on the regional ASF prediction method based on the ordinary kriging interpolation

Di,

Xu,

et al. 2023

Phys. Scr.

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The eLoran system functions as a robust backup to the Global Navigation Satellite System (GNSS), providing substantial signal power, robust anti-jamming capabilities, and an easily maintainable ground system. Nonetheless, the propagation time delay of the system, primarily driven by the Additional Secondary Phase Factor (ASF), significantly influences the positioning accuracy. Compensating for ASF can effectively enhance the positioning performance. Traditional propagation delay theories frequently yield substantial discrepancies between the predicted and measured values in regions characterized by extended propagation distances and varying topographic features. To address this issue, we conducted ASF measurements within a selected test area using a limited number of measurement points. We employed the ordinary Kriging interpolation method to predict ASF values across the entire test area, and used cross-validation to validate our predictions. The results confirmed the accuracy and effectiveness of the Kriging interpolation algorithm in predicting ASF values within specific regions. The cross-validation demonstrated that the errors remained within acceptable ranges. Furthermore, we applied Ordinary Kriging, Inverse Distance Weighting, and Radial Basis Function Interpolation methods to evaluate the positioning accuracy of the test area before and after ASF correction. Compared to other methods, using the Ordinary Kriging interpolation algorithm for predicting ASF values resulted in a corrected positioning accuracy of up to 68.8 m at various test locations. This approach effectively resolves the challenge of low accuracy in theoretical calculations in complex environments. By utilizing ordinary Kriging interpolation, we required measurements of only a few ASF values within a specific region to create an ASF correction map, addressing the challenges related to inaccurate theoretical calculations in complex pathways and avoiding time-consuming and labor-intensive large-scale measurements. The results of this study offer valuable theoretical support for improving the accuracy of land-based navigation systems.

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Section: Kriging Interpolationmentioning

confidence: 99%

Study on the regional ASF prediction method based on the ordinary kriging interpolation

Di,

Xu,

et al. 2023

Phys. Scr.

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show abstract

“…The latter can be related to the fact that for position determination commonly employed approaches are based on time of arrival (TOA), angel of arrival (AOA) or time difference of arrival (TDOA) in case of lower requirements concerning the position accuracy. Further, Direct Position Determination (DPD) [ 22 ], Kalman filter [ 23 ], Particle filter [ 24 ], and laser tracking measurements [ 25 ] are commonly used methods providing higher accuracy.…”

Section: Related Workmentioning

confidence: 99%

Improved Spatiotemporal Framework for Human Activity Recognition in Smart Environment

Salem

Weiss

2022

Sensors

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The rapid development of microsystems technology with the availability of various machine learning algorithms facilitates human activity recognition (HAR) and localization by low-cost and low-complexity systems in various applications related to industry 4.0, healthcare, ambient assisted living as well as tracking and navigation tasks. Previous work, which provided a spatiotemporal framework for HAR by fusing sensor data generated from an inertial measurement unit (IMU) with data obtained by an RGB photodiode for visible light sensing (VLS), already demonstrated promising results for real-time HAR and room identification. Based on these results, we extended the system by applying feature extraction methods of the time and frequency domain to improve considerably the correct determination of common human activities in industrial scenarios in combination with room localization. This increases the correct detection of activities to over 90% accuracy. Furthermore, it is demonstrated that this solution is applicable to real-world operating conditions in ambient light.

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“…Although the United States considered shutting down Loran service in 2010, Congress passed an act to upgrade the Loran system to Enhanced Loran system [6, 7]. Many countries and regions such as the United States, China, Russia, South Korea, Japan, Saudi Arabia and the European Union have upgraded the original Loran system [8–10]. Now, there are more than 50 Enhanced Loran base stations in normal operation all over the world [11].…”

Section: Introductionmentioning

confidence: 99%

Enhanced Loran skywave delay estimation based on artificial neural network in low SNR environment

Zhang

Wan

2020

IET Radar, Sonar & Navigation

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Method to improve accuracy of positioning object by eLoran system with applying standard Kalman filter

Cited by 10 publications

References 6 publications

Study on the regional ASF prediction method based on the ordinary kriging interpolation

Study on the regional ASF prediction method based on the ordinary kriging interpolation

Improved Spatiotemporal Framework for Human Activity Recognition in Smart Environment

Enhanced Loran skywave delay estimation based on artificial neural network in low SNR environment

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