Location Accuracy of INS/Gravity-Integrated Navigation System on the Basis of Ocean Experiment and Simulation

Wang, Hubiao; Wu, Lin; Chai, Hua; Bao, Lifeng; Wang, Yong

doi:10.3390/s17122961

Cited by 17 publications

(4 citation statements)

References 32 publications

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“…While it works in a batch mode, only relatively small amount of data samples are required to obtain a reasonable signal location estimate. This is in contrast to those map matching approaches, i.e., (Wang et al, 2017;Wu et al, 2017) that require enormous measurements to carry out an area based cross correlation.…”

Section: Discussionmentioning

confidence: 91%

Probabilistic Map Matching for Robust Inertial Navigation Aiding

Wang¹,

Gilliam²,

Kealy³

et al. 2022

Preprint

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Robust aiding of inertial navigation systems in GNSS-denied environments is critical for the removal of accumulated navigation error caused by the drift and bias inherent in inertial sensors. One way to perform such an aiding uses matching of geophysical measurements, such as gravimetry, gravity gradiometry or magnetometry, with a known geo-referenced map. Although simple in concept, this map matching procedure is challenging: the measurements themselves are noisy; their associated spatial location is uncertain; and the measurements may match multiple points within the map (i.e. non-unique solution). In this paper, we propose a probabilistic multiple hypotheses tracker to solve the map matching problem and allow robust inertial navigation aiding. Our approach addresses the problem both locally, via probabilistic data association, and temporally by incorporating the underlying platform kinematic constraints into the tracker. The map matching output is then integrated into the navigation system using an unscented Kalman filter. Additionally, we present a statistical measure of local map information densitythe map feature variability -and use it to weight the output covariance of the proposed algorithm. The effectiveness and robustness of the proposed algorithm are demonstrated using a navigation scenario involving gravitational map matching.

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Section: Discussionmentioning

confidence: 91%

Probabilistic Map Matching for Robust Inertial Navigation Aiding

Wang¹,

Gilliam²,

Kealy³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…While it works in a batch mode, only a relatively small amount of data samples is required to obtain a reasonable signal location estimate. This is in contrast to those map-matching approaches, i.e., (H. Wang et al, 2017;Wu et al, 2017) that require enormous measurements to carry out an area-based cross correlation. • A standard ICCP map-matching algorithm, denoted by ICCP-MM, is implemented in this work to compare its performance with that of the proposed PMHT-MM in the simulation using the GGMplus data map.…”

Section: Discussionmentioning

confidence: 92%

Probabilistic Map Matching for Robust Inertial Navigation Aiding

Wang

Gilliam

Kealy

et al. 2023

navi

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In GNSS-denied (or contested) environments, platform navigation performance is dominated by the accuracy of onboard inertial sensors. Even with high-end inertial sensors, which exhibit extremely low bias and drift, it is not possible to avoid the build up of navigation errors over long-time frames (Titterton & Weston, 2004). Removing these accumulated navigation errors is crucial to retain the confidence of navigation accuracy (Groves, 2013). This removal, or correction, is achieved using one or more aiding sources that provide positional information, i.e., a position fix. Aiding sources can be categorized into three groups based on the technologies involved: 1) Radio-based aiding, which uses a transmitted ratio signal to obtain

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“…Gravity measurements with an uncertainty of a few microGalileos are required for using the Watt balance to realize the definition of the kilogram ( 30 ). The use of gravity reference maps to aid inertial marine navigation requires onboard gravimeters with at least milliGalileo accuracy ( 31 ). Seasonal aquifer fluctuations can be monitored by sensing microGalileo-scale gravity changes ( 32 ).…”

Section: Introductionmentioning

confidence: 99%

Gravity surveys using a mobile atom interferometer

et al. 2019

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Mobile gravimetry is important in metrology, navigation, geodesy, and geophysics. Atomic gravimeters could be among the most accurate mobile gravimeters but are currently constrained by being complex and fragile. Here, we demonstrate a mobile atomic gravimeter, measuring tidal gravity variations in the laboratory and surveying gravity in the field. The tidal gravity measurements achieve a sensitivity of 37 μGal/Hz (1 μGal = 10 nm/s2) and a long-term stability of better than 2 μGal, revealing ocean tidal loading effects and recording several distant earthquakes. We survey gravity in the Berkeley Hills with an uncertainty of around 0.04 mGal and determine the density of the subsurface rocks from the vertical gravity gradient. With simplicity and sensitivity, our instrument paves the way for bringing atomic gravimeters to field applications.

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Location Accuracy of INS/Gravity-Integrated Navigation System on the Basis of Ocean Experiment and Simulation

Cited by 17 publications

References 32 publications

Probabilistic Map Matching for Robust Inertial Navigation Aiding

Probabilistic Map Matching for Robust Inertial Navigation Aiding

Probabilistic Map Matching for Robust Inertial Navigation Aiding

Gravity surveys using a mobile atom interferometer

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