INS/GNSS Integration with Compensated Data Synchronization Errors and Displacement of GNSS Antenna. Experience of Practical Realization

Vavilova, N. B.; Golovan, A. A.; Kozlov, Alexander V.; Papusha, I. A.; Zorina, Olga; Izmailov, E. A.; Kukhtevich, S. E.; Фомичев, А. В.

doi:10.1134/s207510872103007x

Cited by 5 publications

(2 citation statements)

References 8 publications

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“…The benefit and methods of estimating the time synchronization errors between INS and GNSS are shown both by numerical simulation of low-cost GNSS-aided INS integration with feedback [ 19 ] and by the processing of real data from aircraft flights with a strapdown INS [ 20 ]. For our research, we have also performed the numerical simulation, which supports the above results (see Section 3.4 ).…”

Section: Theoretical Backgroundmentioning

confidence: 99%

Angular Misalignment Calibration for Dual-Antenna GNSS/IMU Navigation Sensor

Kozlov

Kapralov

2022

Sensors

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We address the angular misalignment calibration problem, which arises when a multi-antenna GNSS serves as a source of aiding information for inertial sensors in an integrated navigation system. Antennas usually occupy some outside structure of the moving carrier object, whilst an inertial measurement unit typically remains inside. Especially when using low- or mid-grade MEMS gyroscopes and accelerometers, it is either impossible or impractical to physically align IMU-sensitive axes and GNSS antenna baselines within some 1–3 degrees due to the micromechanical nature of the inertial sensors: they are just too small to have any physical reference features to align to. However, in some applications, it is desirable to line up all sensors within a fraction-of-a-degree level of accuracy. One may imagine solving this problem via the long-term averaging of sensor signals in different positions to ensure observability and then using angle differences for analytical compensation. We suggest faster calibration in special rotations using sensor fusion. Apart from quicker convergence, this method also accounts for run-to-run inertial sensor bias instability. In addition, it allows further on-the-fly finer calibration in the background when the navigation system performs its regular operation, and carrier objects may undergo gradual deformations of its structure over the years.

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Section: Theoretical Backgroundmentioning

confidence: 99%

Angular Misalignment Calibration for Dual-Antenna GNSS/IMU Navigation Sensor

Kozlov

Kapralov

2022

Sensors

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“…However, in indoor environments, such as underground parking lots and indoor parking buildings where autonomous driving may be involved, the accuracy of positioning technology is severely degraded by factors such as building occlusion and multi-path effects and cannot meet the positioning requirements. (1) Indoor environments are characterized by a more serious signal masking than outdoor environments, a limited openness, a stable and orderly layout, a similar composition of indoor structures, and obstacles with regular characteristics. In indoor environments with these characteristics, mobile platforms must select appropriate indoor positioning methods to meet real-time positioning and navigation requirements.…”

Section: Introductionmentioning

confidence: 99%

Enhanced Indoor Localization Technique Based on Point Cloud Conversion Image Matching

Zhao¹,

He²,

Wang³

et al. 2023

Sensors and Materials

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It is important that indoor autonomous mobile platforms have the capability of localization in general indoor environments. In this study, using multi-threaded vehicle-mounted light detection and ranging (LiDAR), we conducted indoor autonomous mobile platform localization experiments based on a point cloud conversion 2D image method with an interpolated probability distribution, performed a scan matching analysis by converting 2D images based on an interpolated probability distribution while using occupied grid maps, and introduced a multiresolution map method to avoid falling into a local optimum. We found that the method adopted in this study achieves a higher indoor positioning accuracy and a higher matching speed with reduced computational effort while avoiding local optima. Compared with other traditional indoor positioning methods, this method has the advantages of universal applicability and robustness against signal interference and other problems.

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Methodology of Airborne Gravimetry Surveying and Strapdown Gravimeter Data Processing

Golovan

Vyazmin

2023

Gyroscopy Navig.

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The paper presents a methodology of preparing and conducting airborne gravimetry survey using an inertial measuring unit (IMU) or strapdown inertial navigation system as airborne gravimeter. We also discuss the key aspects of developing appropriate postprocessing algorithms and software and their application to gravimeter raw data processing. Solving the strapdown inertial airborne gravimetry problem, we use readings of the IMU sensors (accelerometers and gyroscopes) as primary information. The second equally important source of information is raw (pseudorange, Doppler pseudorange rate, and carrier phase) measurements from the GNSS receivers on board the aircraft and on the ground. The developed methodology, algorithms, and practical recommendations presented in the paper are based on the authors’ long-term experience in airborne gravimetry.

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INS/GNSS Integration with Compensated Data Synchronization Errors and Displacement of GNSS Antenna. Experience of Practical Realization

Cited by 5 publications

References 8 publications

Angular Misalignment Calibration for Dual-Antenna GNSS/IMU Navigation Sensor

Angular Misalignment Calibration for Dual-Antenna GNSS/IMU Navigation Sensor

Enhanced Indoor Localization Technique Based on Point Cloud Conversion Image Matching

Methodology of Airborne Gravimetry Surveying and Strapdown Gravimeter Data Processing

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