{"created":"2025-01-19T01:12:36.897107+00:00","updated":"2025-01-19T17:47:38.024756+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00211455","sets":["1164:4061:10490:10587"]},"path":["10587"],"owner":"44499","recid":"211455","title":["Preliminary Investigation of Using GPS Information to Improve Indoor Pedestrian Dead Reckoning"],"pubdate":{"attribute_name":"公開日","attribute_value":"2021-05-27"},"_buckets":{"deposit":"89be6435-00f4-4a2d-a877-9442787c627f"},"_deposit":{"id":"211455","pid":{"type":"depid","value":"211455","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"Preliminary Investigation of Using GPS Information to Improve Indoor Pedestrian Dead Reckoning","author_link":["537289","537288","537291","537290"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"Preliminary Investigation of Using GPS Information to Improve Indoor Pedestrian Dead Reckoning"},{"subitem_title":"Preliminary Investigation of Using GPS Information to Improve Indoor Pedestrian Dead Reckoning","subitem_title_language":"en"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"位置推定，都市","subitem_subject_scheme":"Other"}]},"item_type_id":"4","publish_date":"2021-05-27","item_4_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"Department of Multimedia Engineering, Graduate School of Information Science and Technology, Osaka University"},{"subitem_text_value":"Department of Multimedia Engineering, Graduate School of Information Science and Technology, Osaka University"}]},"item_4_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"Department of Multimedia Engineering, Graduate School of Information Science and Technology, Osaka University","subitem_text_language":"en"},{"subitem_text_value":"Department of Multimedia Engineering, Graduate School of Information Science and Technology, Osaka University","subitem_text_language":"en"}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"eng"}]},"item_publisher":{"attribute_name":"出版者","attribute_value_mlt":[{"subitem_publisher":"情報処理学会","subitem_publisher_language":"ja"}]},"publish_status":"0","weko_shared_id":-1,"item_file_price":{"attribute_name":"Billing file","attribute_type":"file","attribute_value_mlt":[{"url":{"url":"https://ipsj.ixsq.nii.ac.jp/record/211455/files/IPSJ-UBI21070009.pdf","label":"IPSJ-UBI21070009.pdf"},"date":[{"dateType":"Available","dateValue":"2023-05-27"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-UBI21070009.pdf","filesize":[{"value":"1.9 MB"}],"mimetype":"application/pdf","priceinfo":[{"tax":["include_tax"],"price":"660","billingrole":"5"},{"tax":["include_tax"],"price":"330","billingrole":"6"},{"tax":["include_tax"],"price":"0","billingrole":"36"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_date","version_id":"8c8592fd-6108-472d-a409-fdf72feaee5f","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2021 by the Information Processing Society of Japan"}]},"item_4_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Heng, Zhou"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Takuya, Maekawa"}],"nameIdentifiers":[{}]}]},"item_4_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Heng, Zhou","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Takuya, Maekawa","creatorNameLang":"en"}],"nameIdentifiers":[{}]}]},"item_4_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AA11838947","subitem_source_identifier_type":"NCID"}]},"item_4_textarea_12":{"attribute_name":"Notice","attribute_value_mlt":[{"subitem_textarea_value":"SIG Technical Reports are nonrefereed and hence may later appear in any journals, conferences, symposia, etc."}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourceuri":"http://purl.org/coar/resource_type/c_18gh","resourcetype":"technical report"}]},"item_4_source_id_11":{"attribute_name":"ISSN","attribute_value_mlt":[{"subitem_source_identifier":"2188-8698","subitem_source_identifier_type":"ISSN"}]},"item_4_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"This study presents a method for improving the accuracy of conventional Pedestrian Dead Reckoning (PDR) using GPS satellite information in indoor environments. The accuracy of PDR is limited by the performance of inertial sensors because the errors caused by users' stride prediction and drift of a gyroscope continuously accumulate, resulting in large errors in predicted trajectories. We employ a neural network based PDR which mainly uses accelerometer and gyroscope embedded in a smartphone to predict the user's trajectories. To fix PDR's error on time, we use some landmarks which can be detected by another neural network that leverages GPS satellite information such as S/N ratio and azimuthal angles to predict if the user is close to windows in a building. Then, we fuse these two predictions based on the particle filter to predict a more accurate user's trajectory. We evaluated our framework using data obtained in different buildings in our campus and confirmed the effectiveness of the framework.","subitem_description_type":"Other"}]},"item_4_description_8":{"attribute_name":"論文抄録(英)","attribute_value_mlt":[{"subitem_description":"This study presents a method for improving the accuracy of conventional Pedestrian Dead Reckoning (PDR) using GPS satellite information in indoor environments. The accuracy of PDR is limited by the performance of inertial sensors because the errors caused by users' stride prediction and drift of a gyroscope continuously accumulate, resulting in large errors in predicted trajectories. We employ a neural network based PDR which mainly uses accelerometer and gyroscope embedded in a smartphone to predict the user's trajectories. To fix PDR's error on time, we use some landmarks which can be detected by another neural network that leverages GPS satellite information such as S/N ratio and azimuthal angles to predict if the user is close to windows in a building. Then, we fuse these two predictions based on the particle filter to predict a more accurate user's trajectory. We evaluated our framework using data obtained in different buildings in our campus and confirmed the effectiveness of the framework.","subitem_description_type":"Other"}]},"item_4_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"8","bibliographic_titles":[{"bibliographic_title":"研究報告ユビキタスコンピューティングシステム（UBI）"}],"bibliographicPageStart":"1","bibliographicIssueDates":{"bibliographicIssueDate":"2021-05-27","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"9","bibliographicVolumeNumber":"2021-UBI-70"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"id":211455,"links":{}}