Research on matching area selection based on multiple features fusion of full tensor gravity gradient

Tang, Jianqiao; Xiong, Liansong; Li, Kaihan; Ma, Jie

doi:10.1117/12.2204828

Cited by 3 publications

(4 citation statements)

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“…(g(i, j + 1) + g(i + 1, j + 1) − g(i, j) − g(i + 1, j), σ s represents the standard deviation of the slope, S represents the slope of the gravity field,S φ is the slope in the latitudinal direction, S λ is the slope in the longitudinal direction, scGrid is the grid side length, and S is the average slope in the calculation window. (11) To calculate the gravity anomaly difference entropy, the formula is as follows:…”

Section: Gravity Field Feature Parametersmentioning

confidence: 99%

“…Due to the close correlation between the calibration results of the suitable areas and the selection of the starting point, in order to make the calibration results more accurate, we selected three starting points for trajectory matching on four equal differences in each direction. The starting point for the east-west heading was (3500,3500), (3500,7500), (3500,1150); the starting point for the northeast heading was (3000,3000), (3000,4600), (3000,6200); the starting point for the north-south heading was (3500,3500), (7500,3500), (11,500,3500); and the starting point for the northwest heading was (9000,3000), (9000,4600), (9000,6200). In the experiment, all the suitable areas for trajectory matching calibration from three starting points were considered to be suitable.…”

Section: Validation and Application Of The New Gravity Field Feature ...mentioning

confidence: 99%

“…The local suitable area was generated by simplifying the skeleton points and distance values [10]. In 2015, Jianqiao Tang et al extracted and counted multiple characteristic parameters of the five independent components of the full tensor gravity gradient information and analyzed the influence of the characteristic parameters on the matching performance [11]. In 2016, Yuanyue Ma et al calculated the information entropy of multiple characteristic parameters of the gravity field, set the threshold of the characteristic information entropy of the gravity field, and then divided the suitable area [12].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Optimizing the Matching Area for Underwater Gravity Matching Navigation Based on a New Gravity Field Feature Parameters Selection Method

Zhao,

Zheng,

et al. 2024

Remote Sensing

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This article mainly studies the selection of the matching area in gravity matching navigation systems of underwater vehicles. Firstly, we comprehensively consider 14 types of gravity field feature parameters, and a new gravity field feature parameters selection method is proposed based on feature selection principles and support vector machine algorithms. Secondly, according to the new gravity field feature parameters selection method, the five feature parameters, including range, pooling difference, standard deviation of gravity anomaly, roughness, and correlation coefficient, were selected from the 14 gravity field features parameters. The selected five feature parameters are integrated using SVM, and a classification model is constructed with carefully chosen training and testing sets and parameters for validation. Based on the experimental results, compared to the pre-calibrated results, the classification accuracy of the testing set reaches 91%, demonstrating the effectiveness of the gravity field feature parameter selection method in distinguishing between the suitable and the unsuitable areas. Finally, this method is applied to another area, and we carried out navigation experiments in the areas that were suitable areas in all four directions, as not all areas were suitable in four directions. The results showed that the areas that were suitable in all four directions provided better matching effects, the mean positioning accuracy was less than 100 m, and the accuracy was more than 90%. In path planning, priority can be given to areas that are suitable in all four directions.

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Section: Gravity Field Feature Parametersmentioning

confidence: 99%

Section: Validation and Application Of The New Gravity Field Feature ...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Optimizing the Matching Area for Underwater Gravity Matching Navigation Based on a New Gravity Field Feature Parameters Selection Method

Zhao,

Zheng,

et al. 2024

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…The local standard deviation, local energy, local entropy, roughness, slope, and correlation coefficient parameters were mainly used. The threshold of local standard deviation and local entropy was obtained by the experimental method, which was used as the selection criterion of the suitable area [8]. In 2016, based on the theory of fractal geometry, Bo Wang et al proposed the isotropic coefficient by analyzing the spectral characteristics of the three-dimensional surface gravity anomaly sequence.…”

Section: Introductionmentioning

confidence: 99%

Optimizing Matching Area for Underwater Gravity-Aided Inertial Navigation Based on the Convolution Slop Parameter-Support Vector Machine Combined Method

Wang

Zheng

2021

Remote Sensing

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This paper focuses on the selection of matching areas in the gravity-aided inertial navigation system. Firstly, the Sobel operator was used in convolution of the gravity anomaly map to obtain the feature map. The convolution slope parameters were constructed by combining the feature map and the gravity anomaly map. The characteristic parameters, such as the difference between convolution rows and columns, convolution variance of the feature map, the pooling difference, and range of the gravity anomaly map, were combined. Based on the support vector machine algorithm, the convolution slope parameter–support vector machine combined method is proposed. Second, we selected the appropriate training sample set and set parameters to verify. The results show that compared with the pre-calibration results, the classification accuracy of the test set is more than 92%, which proves that the convolution slope parameter–support vector machine combined method can effectively distinguish between the suitable and the unsuitable area. Thirdly, we applied this method to another region. The navigation experiment was performed in the split-matching area. The average positioning error was better than 100 m, and the correct rate was more than 90%. The results show that sailing in the selected area can accurately match the trajectory and reduce the positioning error.

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Wavelet Transform Based Morphological Matching Area Selection for Underwater Gravity Gradient-Aided Navigation

Wang

Deng

et al. 2023

IEEE Trans. Veh. Technol.

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Research on matching area selection based on multiple features fusion of full tensor gravity gradient

Cited by 3 publications

References 0 publications

Optimizing the Matching Area for Underwater Gravity Matching Navigation Based on a New Gravity Field Feature Parameters Selection Method

Optimizing the Matching Area for Underwater Gravity Matching Navigation Based on a New Gravity Field Feature Parameters Selection Method

Optimizing Matching Area for Underwater Gravity-Aided Inertial Navigation Based on the Convolution Slop Parameter-Support Vector Machine Combined Method

Wavelet Transform Based Morphological Matching Area Selection for Underwater Gravity Gradient-Aided Navigation

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