Asset Details
Do you wish to reserve the book?
A Novel Arithmetic Optimization PDR Algorithm for Smartphones
Hey, we have placed the reservation for you!
By the way, why not check out events that you can attend while you pick your title.
Oops! Something went wrong.
Looks like we were not able to place the reservation. Kindly try again later.
Are you sure you want to remove the book from the shelf?
A Novel Arithmetic Optimization PDR Algorithm for Smartphones
Do you wish to request the book?
A Novel Arithmetic Optimization PDR Algorithm for Smartphones
Please be aware that the book you have requested cannot be checked out. If you would like to checkout this book, you can reserve another copy
We have requested the book for you!
Your request is successful and it will be processed during the Library working hours. Please check the status of your request in My Requests.
Oops! Something went wrong.
Looks like we were not able to place your request. Kindly try again later.
Journal Article
A Novel Arithmetic Optimization PDR Algorithm for Smartphones
2025
Overview
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.
Publisher
MDPI AG,Multidisciplinary Digital Publishing Institute (MDPI)
