A co‐occurrence matrix‐based matching area selection algorithm for underwater gravity‐aided inertial navigation

Wang, Chenglong; Wang, Bo; Deng, Zhihong; Fu, Mengyin

doi:10.1049/rsn2.12021

Cited by 7 publications

(5 citation statements)

References 19 publications

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“…To investigate the relationship between gravity anomaly features and suitable areas, 15 existing gravity features are used to construct a gravity anomaly feature set. The feature set includes standard deviation (F1), roughness (F2), slope (F3), slope standard deviation (F4), correlation coefficient (F5), kurtosis (F6), skewness (F7), range (F8), and mean (F9), and gray histogram complexity (F10), the sum of gravity anomaly gradient values (F11), energy (F12), Contrast (F13), inverse differential moment (F14), and Correlation (F15) in the field of image processing [9,10] . In addition, the sample data is standardized by z-score standardization to eliminate the difference caused by the value range or different dimensions of different feature parameters.…”

Section: The Gravity Anomaly Feature Setmentioning

confidence: 99%

Intelligent Selection Method for Gravity-suitable Area Based on Improved Support Vector Machine by the Genetic Algorithm

He,

Zhang,

et al. 2023

J. Phys.: Conf. Ser.

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The gravity-aided inertial navigation system (GAINS) can achieve precise positioning of underwater vehicles in a gravity-suitable area. However, there are generally shortcomings in the existing intelligent suitable area selection methods in terms of selecting gravity feature parameters and learning parameters. In this paper, an intelligent suitable area selection method is proposed based on an improved support vector machine by the genetic algorithm (GA-SVM) to address the aforementioned problems. Firstly, the genetic algorithm (GA) is utilized to independently pick out the optimal feature subset of 15 existing gravity feature parameters and obtain the optimal support vector machine (SVM) learning parameters while eliminating irrelevant redundant features, thus improving the classifier’s performance and generalization ability. Then, the SVM classifier is trained according to the optimal information output by GA, and the accuracy of the test set is 95.5%. Finally, the classifier is utilized to distinguish suitable and unsuitable areas in the application region to evaluate the proposed method’s performance. The TERCOM experiment in the suitable area resulted in an average positioning error of less than 170 m.

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Section: The Gravity Anomaly Feature Setmentioning

confidence: 99%

Intelligent Selection Method for Gravity-suitable Area Based on Improved Support Vector Machine by the Genetic Algorithm

He,

Zhang,

et al. 2023

J. Phys.: Conf. Ser.

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“…The four spatial relationship feature parameters are weighted and summed, and the comprehensive spatial feature parameters are established. The matching area is selected based on the criterion of maximizing the variance between classes [15]. In 2023, Zou, Jiasheng et al used the SPEARMAN-DEMATEL-ANP structural evaluation method to combine traditional gravity characteristic parameters with fractal geometric parameters for selecting gravity matching adaptation zones [16].…”

Section: Introductionmentioning

confidence: 99%

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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“…High-precision gravity data can be leveraged to study geodynamic and seismic processes, mineral resource exploration, and environmental science (Wang et al, 2004;Sun, 2017). It can be subdivided into various industry applications, such as satellite orbits, long-range missile strikes, and other national defence and military fields (Liu, 2019;Xi et al, 2019;Wang et al, 2020), submarine navigation, and even high-precision autonomous navigation fields (Wu, 2011). It also includes disaster prevention fields, such as Earth observation from space and natural disaster monitoring.…”

Section: Introductionmentioning

confidence: 99%

“…This method uses an absolute gravimeter to measure the change in gravity of the gravitational reference point, and mobile and continuous gravity measurements to improve the spatial and temporal resolution, respectively (Furuya et al, 2003;Hinderer et al, 2015;Fu and She, 2017;Zhu et al, 2017). Based on an error correction model that has gradually evolved in recent years, the high-precision planar gravity value obtained using comprehensive data correction has been successfully applied in research on groundwater level change (Sun, 2017;Xi et al, 2019), mechanisms of mountain uplift (Wang et al, 2014;Schilling et al, 2017), geoids (Liu et al, 2019), subsidence of urban surfaces (Wang et al, 2020), and volcanic activities (Wu, 2011), with abundant results. However, improving the observation accuracy of gravity values requires not only improvements in measurement resolution, but also the removal of the interference factors such as environmental factors.…”

Section: Introductionmentioning

confidence: 99%

Influence of Multi-source Data Analysis on Absolute Gravity Observations: Case Study of Jiufeng, Wuhan

Zhou¹,

Liu²,

Zhang³

et al. 2023

Preprint

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The influence of the background environment on absolute gravity values cannot be ignored. Based on multi-source data between 2013 and 2023 (including absolute gravity, Global Navigation Satellite System (GNSS) observation, underground fluid and rainfall, and vertical gravity gradient data), this paper calculated the influence of changes in the background environment on absolute gravity values and conducted linear fitting and analysis. The results indicated that the average annual rate of change in absolute gravity, underground fluid, and surface deformation at the Wuhan Jiufeng Seismic Station was − 0.1141 µGal/y, -0.5233m/y, and − 0.0016mm/y, respectively. Changes caused by various environmental factors were deducted from the measured absolute gravity values to obtain relatively reasonable and accurate absolute gravity values. Based on the research data and results, the trend of the underground movements of materials was analysed. This provides a theoretical basis for error correction and gravitational reference values for calibrating absolute gravity values at the Wuhan Jiufeng Seismic Station in the future.

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A co‐occurrence matrix‐based matching area selection algorithm for underwater gravity‐aided inertial navigation

Cited by 7 publications

References 19 publications

Intelligent Selection Method for Gravity-suitable Area Based on Improved Support Vector Machine by the Genetic Algorithm

Intelligent Selection Method for Gravity-suitable Area Based on Improved Support Vector Machine by the Genetic Algorithm

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

Influence of Multi-source Data Analysis on Absolute Gravity Observations: Case Study of Jiufeng, Wuhan

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