{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,22]],"date-time":"2025-02-22T05:28:40Z","timestamp":1740202120565,"version":"3.37.3"},"reference-count":0,"publisher":"IOS Press","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2017]]},"abstract":"<jats:p>Dead reckoning has a significant purpose in pedestrian navigation algorithms with assumption of accurate heading estimation. This paper proposes a heading estimation on real time compensation on the basis of region partition particle filter for pedestrian navigation system (RPPF). The RPPF algorithm computes heading correction in real-time using a particle filter. It establishes a functional relationship between the movement of stochastic pedestrian and the regular hexagonal heading constraint; afterwards, it realizes the heading compensation with hexagonal constraint and enhances the heading accuracy. In order implement the proposed algorithm, we conducted a walking experiment of closed curves; the comparative result with the traditional strap-down attitude algorithm, the RPPF enables the effective reduction in the heading error and truly reflects the trajectory curve of pedestrian. The positioning error of the RPPF is less than 2% of the travel distance, which can meet the positioning requirements of pedestrian.<\/jats:p>","DOI":"10.3233\/978-1-61499-828-0-125","type":"book-chapter","created":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T10:21:00Z","timestamp":1740133260000},"source":"Crossref","is-referenced-by-count":0,"title":["Heading Estimation Algorithm Based on Region Partition Particle Filter for Pedestrian Navigation"],"prefix":"10.3233","author":[{"family":"Fei Chengyu","sequence":"additional","affiliation":[]},{"family":"Su Zhong","sequence":"additional","affiliation":[]},{"family":"Li Qing","sequence":"additional","affiliation":[]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","Fuzzy Systems and Data Mining III"],"original-title":[],"deposited":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T11:34:37Z","timestamp":1740137677000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.medra.org\/servlet\/aliasResolver?alias=iospressISBN&isbn=978-1-61499-827-3&spage=125&doi=10.3233\/978-1-61499-828-0-125"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017]]},"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/978-1-61499-828-0-125","relation":{},"ISSN":["0922-6389"],"issn-type":[{"value":"0922-6389","type":"print"}],"subject":[],"published":{"date-parts":[[2017]]}}}