{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T19:40:36Z","timestamp":1790106036173,"version":"4.0.1"},"reference-count":41,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2025,11,21]],"date-time":"2025-11-21T00:00:00Z","timestamp":1763683200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["42030109"],"award-info":[{"award-number":["42030109"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["42074012"],"award-info":[{"award-number":["42074012"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In order to accurately and reasonably set the Pedestrian Dead Reckoning (PDR) system parameters, a novel arithmetic optimization PDR algorithm (AO-PDR) for smartphones is proposed. Firstly, the AO-PDR sets system parameters such as the binary threshold, sliding window size, step length estimation coefficient, and motion state judgment threshold. Based on the positioning error, step deviation, and step length deviation the fitness function of Arithmetic Optimization Algorithm (AOA) is established. Secondly, throughout the initial exploration and development stages, the AOA efficiently searches for the minimum fitness and obtains the optimal system parameters, which are then applied to step detection, step length estimation, and heading correction to solve the pedestrian gait, step length, and heading. Based on the pedestrian motion state, the heading correction mechanism is established. Finally, the pedestrian coordinates are calculated based on the step length and heading. In order to comprehensively evaluate the performance of AO-PDR, four experimenters walked around two experimental sites with three smartphones, respectively, and collected 24 sets of data. The parameter optimization and pedestrian positioning experiments were designed. The experimental results show that AO-PDR can obtain the optimal parameters efficiently and accurately. The mean optimal fitness is 1.352, and the mean running time is 164.85 s. The AO-PDR has high adaptability, efficiency, and stability for different pedestrians and smartphones. The mean positioning error is 0.2893 m, and the standard deviation of positioning error is 0.341 m, which meets the accuracy requirements of pedestrian location-based services.<\/jats:p>","DOI":"10.3390\/s25237129","type":"journal-article","created":{"date-parts":[[2025,11,21]],"date-time":"2025-11-21T17:28:07Z","timestamp":1763746087000},"page":"7129","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["A Novel Arithmetic Optimization PDR Algorithm for Smartphones"],"prefix":"10.3390","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-0288-9697","authenticated-orcid":false,"given":"Mingze","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Geomatics, Liaoning Technical University, Fuxin 123000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Aigong","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Geomatics, Liaoning Technical University, Fuxin 123000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,11,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TIM.2022.3168896","article-title":"High-rate attitude determination of moving vehicles with GNSS: GPS, BDS, GLONASS, and Galileo","volume":"71","author":"Shu","year":"2022","journal-title":"IEEE Trans. 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