{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,23]],"date-time":"2026-03-23T18:56:48Z","timestamp":1774292208572,"version":"3.50.1"},"reference-count":53,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2018,5,1]],"date-time":"2018-05-01T00:00:00Z","timestamp":1525132800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key Research of China","award":["2016YFB0502202"],"award-info":[{"award-number":["2016YFB0502202"]}]},{"name":"National Key Research of China","award":["2016YFB0501803"],"award-info":[{"award-number":["2016YFB0501803"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>This paper proposes a pedestrian dead reckoning (PDR) algorithm based on the strap-down inertial navigation system (SINS) using the gyros, accelerometers, and magnetometers on smartphones. In addition to using a gravity vector, magnetic field vector, and quasi-static attitude, this algorithm employs a gait model and motion constraint to provide pseudo-measurements (i.e., three-dimensional velocity and two-dimensional position increment) instead of using only pseudo-velocity measurement for a more robust PDR algorithm. Several walking tests show that the advanced algorithm can maintain good position estimation compare to the existing SINS-based PDR method in the four basic smartphone positions, i.e., handheld, calling near the ear, swaying in the hand, and in a pants pocket. In addition, we analyze the navigation performance difference between the advanced algorithm and the existing gait-model-based PDR algorithm from three aspects, i.e., heading estimation, position estimation, and step detection failure, in the four basic phone positions. Test results show that the proposed algorithm achieves better position estimation when a pedestrian holds a smartphone in a swaying hand and step detection is unsuccessful.<\/jats:p>","DOI":"10.3390\/s18051391","type":"journal-article","created":{"date-parts":[[2018,5,3]],"date-time":"2018-05-03T03:20:27Z","timestamp":1525317627000},"page":"1391","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":137,"title":["Robust Pedestrian Dead Reckoning Based on MEMS-IMU for Smartphones"],"prefix":"10.3390","volume":"18","author":[{"given":"Jian","family":"Kuang","sequence":"first","affiliation":[{"name":"GNSS Research Center, Wuhan University, 129 Luoyu Road, Wuhan 430079, China"}]},{"given":"Xiaoji","family":"Niu","sequence":"additional","affiliation":[{"name":"GNSS Research Center, Wuhan University, 129 Luoyu Road, Wuhan 430079, China"}]},{"given":"Xingeng","family":"Chen","sequence":"additional","affiliation":[{"name":"GNSS Research Center, Wuhan University, 129 Luoyu Road, Wuhan 430079, China"}]}],"member":"1968","published-online":{"date-parts":[[2018,5,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"244","DOI":"10.1109\/JIOT.2015.2506258","article-title":"An indoor location-aware system for an IoT-based smart museum","volume":"3","author":"Alletto","year":"2016","journal-title":"IEEE Internet Things J."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"747","DOI":"10.3390\/mi6060747","article-title":"WiFi-Aided Magnetic Matching for Indoor Navigation with Consumer Portable Devices","volume":"6","author":"Li","year":"2015","journal-title":"Micromachines"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"4034","DOI":"10.1109\/JSEN.2014.2330573","article-title":"Intelligent Fusion of Wi-Fi and Inertial Sensor-Based Positioning Systems for Indoor Pedestrian Navigation","volume":"14","author":"Chen","year":"2014","journal-title":"IEEE Sens. 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