{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,30]],"date-time":"2025-12-30T08:53:52Z","timestamp":1767084832475,"version":"build-2065373602"},"reference-count":26,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2021,10,1]],"date-time":"2021-10-01T00:00:00Z","timestamp":1633046400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China under Grant","award":["41774014, 41574014"],"award-info":[{"award-number":["41774014, 41574014"]}]},{"name":"Liaoning Revitalization Talents Program under Grant","award":["XLYC2002082"],"award-info":[{"award-number":["XLYC2002082"]}]},{"name":"Frontier Science and Technology Innovation Project and the Innovation Workstation Project of Science and Technology Commission of the Central Military Commission under Grant","award":["085015"],"award-info":[{"award-number":["085015"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/rs13193940","type":"journal-article","created":{"date-parts":[[2021,10,8]],"date-time":"2021-10-08T21:26:20Z","timestamp":1633728380000},"page":"3940","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Optimizing Matching Area for Underwater Gravity-Aided Inertial Navigation Based on the Convolution Slop Parameter-Support Vector Machine Combined Method"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3782-624X","authenticated-orcid":false,"given":"Shuoqi","family":"Wang","sequence":"first","affiliation":[{"name":"Qian Xuesen Laboratory of Space Technology, China Academy of Space Technology, Beijing 100094, China"}]},{"given":"Wei","family":"Zheng","sequence":"additional","affiliation":[{"name":"Qian Xuesen Laboratory of Space Technology, China Academy of Space Technology, Beijing 100094, China"},{"name":"School of Geomatics, Liaoning Technical University, Fuxin 123000, China"},{"name":"School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin 150001, China"}]},{"given":"Zhaowei","family":"Li","sequence":"additional","affiliation":[{"name":"Qian Xuesen Laboratory of Space Technology, China Academy of Space Technology, Beijing 100094, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,10,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"73957","DOI":"10.1109\/ACCESS.2020.2981973","article-title":"Link Adaptation on an Underwater Communications Network Using Machine Learning Algorithms: Boosted Regression Tree Approach","volume":"8","author":"Alamgir","year":"2020","journal-title":"IEEE Access"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"19","DOI":"10.3389\/fnbot.2019.00019","article-title":"Solving gravity anomaly matching problem under large initial errors in gravity aided navigation by using an affine transformation based artificial bee colony algorithm","volume":"13","author":"Dai","year":"2019","journal-title":"Front. 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