Wang, B.; Li, Q.; Liu, J.; Wang, Z.; Yu, Q.; Liang, R. A WKNN Indoor Fingerprint Localization Technique Based on Improved FD’s Discrimination Capability to PD. Preprints2024, 2024011075. https://doi.org/10.20944/preprints202401.1075.v1
APA Style
Wang, B., Li, Q., Liu, J., Wang, Z., Yu, Q., & Liang, R. (2024). A WKNN Indoor Fingerprint Localization Technique Based on Improved FD’s Discrimination Capability to PD. Preprints. https://doi.org/10.20944/preprints202401.1075.v1
Chicago/Turabian Style
Wang, B., Qiudong Yu and Rui Liang. 2024 "A WKNN Indoor Fingerprint Localization Technique Based on Improved FD’s Discrimination Capability to PD" Preprints. https://doi.org/10.20944/preprints202401.1075.v1
Abstract
The weighted K-nearest neighbors (WKNN) algorithm has been widely used in indoor fingerprint localization, which utilizes fingerprint distance (FD) of pairwise positions to discriminate the physical distance (PD) between them. However, due to the varied states of different wireless access points (APs), their contribution to FD discrimination capability varies. In our study, we analyzed several critical causes that affect APs’ contribution, including AP’s health state, the distance, and the direction between APs and position pairs. Inspired by these insights, a threshold was set for all sample RSS to eliminate the impact of abnormal APs on FD, and a correction quantity for each RSS was provided by the distance between APs and sample positions to reduce the strong signal influence on FD. Furthermore, a priority weight was designed by RSS differences (RSSD) to further optimize FD’s capability to discriminate PD. Integrating the above policies, a new indoor fingerprint localization technique is redefined, referred to FD’s discrimination capability improvement WKNN (FDDC-WKNN), which is suitable for indoor scenes with a large number of APs that are not uniformly managed. Our simulation results show that the correlation and consistency between FD and PD are well improved, with strong correlation increasing from 0 to 76% and high consistency increasing from 26% to 99%, which confirms that the proposed policies can greatly enhance FD’s discrimination capabilities to PD, and we also found that abnormal APs can cause significant impact on FDs’ discrimination Capability. Further, by implementing the FDDC-WKNN algorithm in experiments, we obtained the optimal K value in both the simulation scene and real library scene, under which the average localization errors have been reduced from 2.2732m to 1.2183m and from 3.4295m to 2.2068m, respectively. In addition, compared to not using the FDDC-WKNN, the cumulative distribution function (CDF) of the localization errors curve converged faster and the errors fluctuation is smaller, which demonstrate FDDC-WKNN having stronger robustness and more stable localization performance to the state of APs in indoor environments.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
