Overview of architecture for GPS-INS integration

Srinivas, P. V. S.; Kumar, Anil

doi:10.1109/rdcape.2017.8358310

Cited by 16 publications

(7 citation statements)

References 5 publications

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“…Sensor fusion as a computational procedure can take the architecture of three distinct fusion schemes: a) loosely coupled (LC), b) tightly coupled (TC), and c) ultra tightly coupled (UTC). As stated by [191], in GNSS, LC is the simplest type among the three architectures that provides the essential redundancy based on the duplicated information in situations of good visibility (four satellites) to achieve high accuracy. TC is widely adopted because it provides better accuracy and is less susceptible to jamming, in addition to maintaining navigation in situations of poor visibility (fewer than four satellites).…”

Section: ) Sensor Fusionmentioning

confidence: 99%

Position, Navigation, and Timing (PNT) Through Low Earth Orbit (LEO) Satellites: A Survey on Current Status, Challenges, and Opportunities

et al. 2022

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More and more satellites are populating the sky nowadays in the Low Earth orbits (LEO). Most of the targeted applications are related to broadband and narrowband communications, Earth observation, synthetic aperture radar, and internet-of-Things (IoT) connectivity. In addition to these targeted applications, there is yet-to-be-harnessed potential for LEO and positioning, navigation, and timing (PNT) systems, or what is nowadays referred to as LEO-PNT. No commercial LEO-PNT solutions currently exist and there is no unified research on LEO-PNT concepts. Our survey aims to fill the gaps in knowledge regarding what a LEO-PNT system entails, its technical design steps and challenges, what physical layer parameters are viable solutions, what tools can be used for a LEO-PNT design (e.g., optimisation steps, hardware and software simulators, etc.), the existing models of wireless channels for satellite-toground and ground-to-satellite propagation, and the commercial prospects of a future LEO-PNT system. A comprehensive and multidisciplinary survey is provided by a team of authors with complementary expertise in wireless communications, signal processing, navigation and tracking, physics, machine learning, Earth observation, remote sensing, digital economy, and business models.

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Section: ) Sensor Fusionmentioning

confidence: 99%

Position, Navigation, and Timing (PNT) Through Low Earth Orbit (LEO) Satellites: A Survey on Current Status, Challenges, and Opportunities

et al. 2022

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“…According how GNSS measurements are used in the integration algorithm, the way of corrections applied to the inertial navigation solution, and whether the GNSS user equipment is aided, the INS/GNSS integration architecture can be mainly divided into three groups: loosely coupled, tightly coupled and ultra-tightly coupled integration systems [25].…”

Section: Ins/gnss Tightly Coupled Integrationmentioning

confidence: 99%

Enhanced Redundant Measurement-Based Kalman Filter for Measurement Noise Covariance Estimation in INS/GNSS Integration

Zhang

Fu³

et al. 2020

Remote Sensing

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Adaptive Kalman filters (AKF) have been widely applied to the inertial navigation system (INS)/global navigation satellite system (GNSS) integrated navigation system. However, the traditional AKF methods suffer from the problems of filtering instability or covariance underestimation, especially when the GNSS measurement disturbances occur. In this paper, an enhanced redundant measurement-based AKF is developed to improve the filtering performance. The scheme is based on the mutual difference sequence derived from the redundant measurement of INS. By using the mutual difference sequence, the measurement noise covariance can be estimated without being affected by the inaccuracy estimates, hence avoiding the risk of filtering divergence. In addition, the kernel density estimation is used to estimate the GNSS measurement noise’s probability density to detect whether the Gaussian properties of the measurement noise are maintained. When the noise statistics are far from Gaussian distribution, the difference sequence will be modeled as an autoregressive process using the Burg’s method. The real variance of the difference sequence can then be updated relying on the autoregressive model in order to avoid the covariance underestimation. A field experiment was carried out to evaluate the performance of the proposed method. The test results demonstrate that the proposed method can effectively mitigate the GNSS measurement disturbances and improve the accuracy of the navigation solution.

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“…Improving the accuracy of SINS is always one of important tasks for the study of navigation and guidance. Mainstream technology contains IMU error modeling, IMU precise calibration, revolution-modulation technology, optimizing SINS navigation algorithm etc [1][2][3][4]. For these methods, the primary error source of SINS should be determined firstly to assistant finding the best modified direction.…”

Section: Introductionmentioning

confidence: 99%

Analysis Method of Strapdown Inertial Navigation Error Distribution Based on Covariance Matrix Decomposition

Yang¹,

Yan²,

Liu³

et al. 2022

Preprint

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Error distribution analysis is an important assistant technology for the research of SINS (Strapdown Inertial Navigation System). Error distribution result can provide the contribution of different errors to final navigation error, which is helpful for modifying and optimizing SINS. To realize decomposing the navigation error into parts that caused by each error source, the SINS error state space model is established and covariance matrix is decomposed according to error sources. The proposed error distribution analysis method based on 34-dimension SINS error model can quantitatively analyze the contribution to the end navigation error of initial errors, IMU(Inertial Measurement Unit) bias, IMU scale factor errors, mounting errors of gyroscopes and accelerometers, and IMU stochastic errors. The simulations in static condition and single axis rotation condition indict that the distribution result of proposed analysis method accords with the law of error propagation. After trajectory determined, the corresponding error distribution result will be calculated with the proposed method. Compared with the Monte-Carlo method and other method based on covariance matrix, the proposed method uses more complete error model, considers the interaction effect of error sources and can be easily realized with less computation.

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Overview of architecture for GPS-INS integration

Cited by 16 publications

References 5 publications

Position, Navigation, and Timing (PNT) Through Low Earth Orbit (LEO) Satellites: A Survey on Current Status, Challenges, and Opportunities

Position, Navigation, and Timing (PNT) Through Low Earth Orbit (LEO) Satellites: A Survey on Current Status, Challenges, and Opportunities

Enhanced Redundant Measurement-Based Kalman Filter for Measurement Noise Covariance Estimation in INS/GNSS Integration

Analysis Method of Strapdown Inertial Navigation Error Distribution Based on Covariance Matrix Decomposition

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