XIAO Yun, ZHANG Jinbai, CAO Jie, CHEN Kaining, WANG Yukang, HONG Xiaodong. Suitability Analysis of Gravity Matching Navigation Based on Multiple Attribute Decision Making Theory[J]. Geomatics and Information Science of Wuhan University, 2023, 48(7): 1089-1099. DOI: 10.13203/j.whugis20230073
Citation: XIAO Yun, ZHANG Jinbai, CAO Jie, CHEN Kaining, WANG Yukang, HONG Xiaodong. Suitability Analysis of Gravity Matching Navigation Based on Multiple Attribute Decision Making Theory[J]. Geomatics and Information Science of Wuhan University, 2023, 48(7): 1089-1099. DOI: 10.13203/j.whugis20230073

Suitability Analysis of Gravity Matching Navigation Based on Multiple Attribute Decision Making Theory

  •   Objectives  In order to ensure the reliability of underwater gravity matching navigation, a suitability analysis method of gravity matching navigation based on multiple attribute decision making theory is proposed, which is to solve the defect of incomplete evaluation results caused by a single feature index as a suitability analysis criterion.
      Methods  First, six main characteristic parameters, such as standard deviation of gravity field, roughness, correlation coefficient, slope standard deviation, skew coefficient and differential entropy of gravitational anomalies, are used as attribute sets for weighted normalization, so that the decision index is obtained to rank the suitability of the matching region, and the most excellent suitability area is evaluated by constructing the relative fit.Second, in the same conditions, we compare the simulation results of gravity matching in each region to be matched.
      Results  The results show that the matching effect is more stable in the higher decision-making index area, and the most excellent suitability area could be directly screened, The matching degree of decision results with navigation results is as high as 80%, the decision-making results of the method have good consistency with the accuracy of the matching experiment.
      Conclusions  The reliability of the method is high, so it could solve the problem of less information of a single feature index, and provide an important guarantee for the long-term voyage of underwater submersibles.
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