決定木によるGNSS測位の検討

カテゴリ: 論文誌(論文単位)

グループ名: 【C】電子・情報・システム部門

発行日: 2021/05/01

タイトル(英語): Decision Tree Based GNSS Positioning

著者名: 福田　智和（(株)京三製作所），石井　光治（香川大学創造工学部）

著者名(英語): Tomokazu Fukuda (Kyosan Electric MFG. CO., LTD.), Koji Ishii (Faculty of Engineering and Design, Kagawa University)

キーワード: GNSS，機械学習，決定木，ランダムフォレスト　　GNSS，machine learning，decision tree，random forest

要約(英語): Position information and time information provided by GNSS (Global Navigation Satellite System) are positively used. The accuracy of GNSS information is a very important factor for future ICT based systems such as an autonomous driving car, 5G wireless system etc. To meet such a demand, this work applies a machine learning technique to GNSS positioning and shows the feasibility of machine learning based GNSS positioning. As one of the advantages, our proposed system can make full use of current GNSS receiver system, that is, it does not need the modification of current device except for the signal processing architecture. Simulation results show that the proposed decision tree based GNSS positioning can enhance both accuracy and continutity of positioning compared to the conventional technique and the random forest based GNSS positioning can further improve both accuracy and continutity of positioning.

本誌: 電気学会論文誌C（電子・情報・システム部門誌） Vol.141 No.5 （2021） 特集：神経工学

本誌掲載ページ: 704-711 p

原稿種別: 論文／日本語

電子版へのリンク: https://www.jstage.jst.go.jp/article/ieejeiss/141/5/141_704/_article/-char/ja/