Application of Self-Adaptive Artificial Physics Optimized Particle Filter in INS/Gravity Gradient Aided Navigation

Liu, Fanming; Li, Fangming

doi:10.1109/icma.2018.8484416

Cited by 4 publications

(2 citation statements)

References 6 publications

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“…Currently, there are two main categories of gravity navigation algorithms based on their characteristics: sequential-based matching algorithms, such as Terrain Contour Matching (TERCOM) and Iterative Closest Contour Point (ICCP) [9,10], and iterative-based filterrecursive algorithms, such as Sandia Inertial Terrain-Aided Navigation (SITAN) [11] and Particle Filter (PF) [12]. In pursuit of better applications in gravity-aided navigation, numerous scholars have made improvements to existing algorithms or proposed novel ones by integrating artificial intelligence [13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Parallel Multiple Methods with Adaptative Decision Making for Gravity-Aided Navigation

Gao,

Cai

2023

JMSE

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Gravity-aided navigation is an effective navigation method for underwater vehicles. However, the distribution of the gravity field may affect the measurement errors of gravity anomalies and the precision of gravity-aided navigation. In this paper, the upper and lower thresholds of the gravity field standard deviation are computed by the statistical properties of the local gravity field to classify each grid in the gravity map into different levels. A parallel multiple methods with adaptive decision making (PMMADM) for gravity-aided navigation is proposed which incorporates the gravity anomaly measurements, particle filtering, and maximum correlation method into the observation equation of the extended Kalman filter. The algorithm autonomously selects the observation variables in the filter by combining the gravity field standard deviation at the current position of the carrier with a decision tree. This approach can combine the characteristics of different gravity matching algorithms, reduce the impact of random noise in the measurements, and improve the positioning accuracy of gravity-aided navigation. Physical simulation experiments demonstrated that the proposed gravity matching algorithm achieves the high navigation accuracy and long-term stability in different gravity fields, and the mean value of positioning error is 620.72 m.

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Section: Introductionmentioning

confidence: 99%

Parallel Multiple Methods with Adaptative Decision Making for Gravity-Aided Navigation

Gao,

Cai

2023

JMSE

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“…In this framework, the mathematical model needs to be linearized. Some particle filter algorithms have also been considered in the field of gravity gradient matching algorithm [ 21 , 22 ]. In addition, most of the previous literatures studied the mathematical model and performance analysis based on the platform gravity gradiometer, and some factors affecting performance were considered [ 23 , 24 ].…”

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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Real-Time Joint Filtering of Gravity and Gravity Gradient Data Based on Improved Kalman Filter

Yuan,

Qin,

et al. 2024

IEEE Trans. Geosci. Remote Sensing

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Application of Self-Adaptive Artificial Physics Optimized Particle Filter in INS/Gravity Gradient Aided Navigation

Cited by 4 publications

References 6 publications

Parallel Multiple Methods with Adaptative Decision Making for Gravity-Aided Navigation

Parallel Multiple Methods with Adaptative Decision Making for Gravity-Aided Navigation

An Aided Navigation Method Based on Strapdown Gravity Gradiometer

Real-Time Joint Filtering of Gravity and Gravity Gradient Data Based on Improved Kalman Filter

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