Asset Details
Do you wish to reserve the book?
On Fusing Wireless Fingerprints with Pedestrian Dead Reckoning to Improve Indoor Localization Accuracy
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?
On Fusing Wireless Fingerprints with Pedestrian Dead Reckoning to Improve Indoor Localization Accuracy
Do you wish to request the book?
On Fusing Wireless Fingerprints with Pedestrian Dead Reckoning to Improve Indoor Localization Accuracy
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
On Fusing Wireless Fingerprints with Pedestrian Dead Reckoning to Improve Indoor Localization Accuracy
2025
Overview
Accurate indoor positioning remains a critical challenge due to the limitations of single-source systems, such as signal instability and environmental obstructions. This study introduces a multi-source fusion positioning algorithm that integrates inertial sensors and signal fingerprints to address these issues. Using a weighted fusion method, the algorithm employs pedestrian dead reckoning (PDR) for trajectory tracking and combines its outputs with wireless signal fingerprints. Experimental evaluations conducted on diverse trajectories reveal significant improvements in accuracy, achieving a 35.3% enhancement over wireless-only systems and a 71.4% improvement compared to standalone PDR. The proposed method effectively balances computational efficiency and accuracy, demonstrating robustness in complex and dynamic indoor environments. These findings establish the algorithm’s potential for practical applications in navigation, robotics, and Industry 4.0, where precise indoor localization is essential.
