Localization of Multiple Underwater Objects With Gravity Field and Gravity Gradient Tensor

Tang, Jingtian; Hu, Shuanggui; Ren, Zhengyong; Chen, Chaojian

doi:10.1109/lgrs.2017.2784837

Cited by 24 publications

(8 citation statements)

References 21 publications

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“…The gravity gradient is the second derivative of the gravity potential. Relative to gravity, a gravity gradiometer can measure subtle changes of gravity signals in different geographical locations [ 15 , 16 ]. Therefore, gravity gradient signals contain more abundant navigation information.…”

Section: Introductionmentioning

confidence: 99%

An Aided Navigation Method Based on Strapdown Gravity Gradiometer

Gao

Chang

et al. 2021

Sensors

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The gravity gradient is the second derivative of gravity potential. A gravity gradiometer can measure the small change of gravity at two points, which contains more abundant navigation and positioning information than gravity. In order to solve the problem of passive autonomous, long-voyage, and high-precision navigation and positioning of submarines, an aided navigation method based on strapdown gravity gradiometer is proposed. The unscented Kalman filter framework is used to realize the fusion of inertial navigation and gravity gradient information. The performance of aided navigation is analyzed and evaluated from six aspects: long voyage, measurement update period, measurement noise, database noise, initial error, and inertial navigation system device level. When the parameters are set according to the benchmark parameters and after about 10 h of simulation, the results show that the attitude error, velocity error, and position error of the gravity gradiometer aided navigation system are less than 1 arcmin, 0.1 m/s, and 33 m, respectively.

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Section: Introductionmentioning

confidence: 99%

An Aided Navigation Method Based on Strapdown Gravity Gradiometer

Gao

Chang

et al. 2021

Sensors

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“…However, ground-based systems are characterized by limited coverage and insufficient accuracy and depend on the state of the ionosphere [ 3 ]. One promising direction for the creation of alternative systems for independent global navigation is the use of the Earth’s gravity field [ 4 , 5 , 6 ].…”

Section: Introductionmentioning

confidence: 99%

Global Navigation Process Simulation Based on Different Types of Gravity Data

Lytkin¹,

Tarasov²

2020

Sensors

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A theoretical study on the feasibility of global navigation based on three different types of gravity data was performed. A computer simulation of gravity-aided navigation was performed for three models of sections of the Earth’s surface with gravity anomalies distributed as specified. For navigation, three types of data sources were used, e.g., the gravity vector magnitude, three orthogonal projections of the gravity vector, and five independent components of the full gravity tensor. For each data source, when searching a specified route, the dependencies of the number of the identified true and false points were determined in accordance with the measurement error specified. The problem of determining the true route on the set of the identified points is briefly reviewed. General conclusions are presented regarding the practical applicability of the reviewed data sources to the problem of global navigation.

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“…But, over the past decade, rotating accelerometer gravity gradiometers are the only commercial moving base gravity gradiometer in the word; all other types are either in fight testing or in a laboratory setting. Companies that operate commercial rotating accelerometer gravity gradiometer systems are: Bell Geospace (Air-FTG), ARKeX (FTGeX), GEDEX (HD-AGG), and FUGRO (AGG-Falcon) (Rogers 2009;Metzger 1977). In this study, we synthetically consider almost all the unperfect factors, for example accelerometer installation errors, accelerometer scale-factor imbalance, etc., and construct a numerical model.…”

Section: Introductionmentioning

confidence: 99%

Numerical Model of Moving-Base Rotating Accelerometer Gravity Gradiometer

2020

International Association of Geodesy Symposia

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In development of a rotating accelerometer gravity gradiometer (RAGG), it is difficult for us to distinguish the measurement signals and error components in the RAGG output without a verified and correctness RAGG analytical model. In addition, many key techniques, such as RAGG analytical model validation, online error compensation, post motion error compensation, are difficult to be verified. RAGG numerical model can provide validation platform for solving all the above problems, which can speed up the development of the RAGG. In this study, based on the principle and configuration of the RAGG, we synthetically consider almost all the error factors, such as circuit gain mismatch, installation errors, accelerometer scale-factor imbalance, and accelerometer second-order error coefficients, construct a parameters adjustable RAGG numerical model. In multi-frequency gravitational gradient simulation experiment, we use the RAGG numerical model simulating the situation that a test mass rotates about the RAGG with time-varying angular velocity to generate multi-frequency gravitational gradient excitations; the experiment results are consistent with the theoretical ones; the RAGG numerical model can recur some phenomenons of a actual RAGG.

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Localization of Multiple Underwater Objects With Gravity Field and Gravity Gradient Tensor

Cited by 24 publications

References 21 publications

An Aided Navigation Method Based on Strapdown Gravity Gradiometer

An Aided Navigation Method Based on Strapdown Gravity Gradiometer

Global Navigation Process Simulation Based on Different Types of Gravity Data

Numerical Model of Moving-Base Rotating Accelerometer Gravity Gradiometer

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