{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,27]],"date-time":"2025-10-27T16:08:36Z","timestamp":1761581316260,"version":"3.41.2"},"reference-count":28,"publisher":"Emerald","issue":"3","license":[{"start":{"date-parts":[[2015,6,15]],"date-time":"2015-06-15T00:00:00Z","timestamp":1434326400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.emerald.com\/insight\/site-policies"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2015,6,15]]},"abstract":"<jats:sec>\n               <jats:title content-type=\"abstract-heading\">Purpose<\/jats:title>\n               <jats:p> \u2013 The purpose of this paper is to achieve reliable estimation of yaw angles by fusing data from low-cost inertial and magnetic sensing. <\/jats:p>\n            <\/jats:sec>\n            <jats:sec>\n               <jats:title content-type=\"abstract-heading\">Design\/methodology\/approach<\/jats:title>\n               <jats:p> \u2013 In this paper, yaw angle is estimated by fusing inertial and magnetic sensing from a digital compass and a gyroscope, respectively. A Kalman filter estimates the error produced by the gyroscope. <\/jats:p>\n            <\/jats:sec>\n            <jats:sec>\n               <jats:title content-type=\"abstract-heading\">Findings<\/jats:title>\n               <jats:p> \u2013 Drift effect produced by the gyroscope is significantly reduced and, at the same time, the system has the ability to react quickly to orientation changes. The system combines the best of each sensor, the stability of the magnetic sensor and the fast response of the inertial sensor. <\/jats:p>\n            <\/jats:sec>\n            <jats:sec>\n               <jats:title content-type=\"abstract-heading\">Research limitations\/implications<\/jats:title>\n               <jats:p> \u2013 The system does not present a stable behavior in the presence of large vibrations. Considerable calibration efforts are needed. <\/jats:p>\n            <\/jats:sec>\n            <jats:sec>\n               <jats:title content-type=\"abstract-heading\">Practical implications<\/jats:title>\n               <jats:p> \u2013 Today, most of human\u2013robot interaction technologies need to have the ability to estimate orientation, especially yaw angle, from small-sized and low-cost sensors. <\/jats:p>\n            <\/jats:sec>\n            <jats:sec>\n               <jats:title content-type=\"abstract-heading\">Originality\/value<\/jats:title>\n               <jats:p> \u2013 Existing methods for inertial and magnetic sensor fusion are combined to achieve reliable estimation of yaw angle. Experimental tests in a human\u2013robot interaction scenario show the performance of the system.<\/jats:p>\n            <\/jats:sec>","DOI":"10.1108\/sr-10-2014-0723","type":"journal-article","created":{"date-parts":[[2015,6,8]],"date-time":"2015-06-08T04:12:08Z","timestamp":1433736728000},"page":"244-250","source":"Crossref","is-referenced-by-count":12,"title":["Kalman filter-based yaw angle estimation by fusing inertial and magnetic sensing: a case study using low cost sensors"],"prefix":"10.1108","volume":"35","author":[{"given":"Pedro","family":"Neto","sequence":"first","affiliation":[]},{"given":"Nuno","family":"Mendes","sequence":"additional","affiliation":[]},{"given":"A. 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