海洋测绘2026，Vol.46Issue(3)：35-39,5.DOI:10.3969/j.issn.1671-3044.2026.03.008
基于多特征融合DNN的重力导航适配性定量评估
Quantitative assessment of gravity navigation suitability based on multi-feature fusion and deep neural networks
摘要
Abstract
To address the suitability assessment requirements in long-endurance underwater gravity matching navigation,this paper proposes a quantitative analysis method based on multi-feature fusion and a deep neural network(DNN).Multiple representative gravity field feature parameters are integrated,and a DNN prediction model is constructed using matching probability and mean matching error as regression targets.This approach enables the transformation of suitability region analysis from qualitative evaluation to quantitative prediction.Experimental results demonstrate that,compared with traditional suitability assessment methods based on single features,the proposed multi-feature fusion approach reduces mean matching error and improves matching probability prediction accuracy by approximately 20%.Moreover,the model maintains favorable stability and reliability under limited sample conditions.The results demonstrate the effectiveness of the multi-feature fusion DNN framework in quantitatively assessing gravity matching navigation suitability,and provide informational support for underwater gravity navigation area selection and trajectory planning.关键词
重力匹配导航/重力导航适配性评估/综合特征参数/深度神经网络/定量评估Key words
gravity matching navigation/gravity navigation suitability assessment/comprehensive feature parameters/deep neural network/quantitative assessment分类
天文与地球科学引用本文复制引用
刘雨杭,刘会,江瑞,姚竹,程默非..基于多特征融合DNN的重力导航适配性定量评估[J].海洋测绘,2026,46(3):35-39,5.基金项目
国家自然科学基金(42404100) （42404100）
江苏省基金青年科学基金项目(BK20241059) （BK20241059）
江苏省青年科技人才托举工程(JSTJ-2025-585) （JSTJ-2025-585）
自然资源部海洋测绘重点实验室开放研究基金(2024B14) （2024B14）
地理信息工程国家重点实验室测绘科学与地球空间信息技术自然资源部重点实验室联合开放基金(NO2024-01-08). （NO2024-01-08）
