Drone-Assisted Fingerprint Localization Based on Kernel Global Locally Preserving Projection
To improve the limited number of fixed access points (APs) and the inability to dynamically adjust them in fingerprint localization, this paper attempted to use drones to replace these APs. Drones have higher flexibility and accuracy, can hover in different locations, and can adapt to different scen...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-07-01
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| Series: | Drones |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2504-446X/7/7/480 |
| _version_ | 1797589598122016768 |
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| author | Mengxing Pan Yunfei Li Weiqiang Tan Wengen Gao |
| author_facet | Mengxing Pan Yunfei Li Weiqiang Tan Wengen Gao |
| author_sort | Mengxing Pan |
| collection | DOAJ |
| description | To improve the limited number of fixed access points (APs) and the inability to dynamically adjust them in fingerprint localization, this paper attempted to use drones to replace these APs. Drones have higher flexibility and accuracy, can hover in different locations, and can adapt to different scenarios and user needs, thereby improving localization accuracy. When performing fingerprint localization, it is often necessary to consider various factors such as environmental complexity, large-scale raw data collection, and signal strength variation. These factors can lead to high-dimensional and complex nonlinear relationships in location fingerprints, thereby greatly affecting localization accuracy. In order to overcome these problems, this paper proposes a kernel global locally preserving projection (KGLPP) algorithm. The algorithm can reduce the dimensionality of location fingerprint data while preserving its most-important structural information, and it combines global and local information to avoid the problem of reduced information and poor dimensionality reduction effects, which may arise from considering only one. In the process of location estimation, an improved weighted <i>k</i>-nearest neighbor (IWKNN) algorithm is adopted to more accurately estimate the target’s position. Unlike the traditional KNN or WKNN algorithms, the IWKNN algorithm can choose the optimal number of nearest neighbors autonomously, perform location estimation and weight calculation based on the actual situation, and thus, obtain more-accurate location estimation results. The experimental results showed that the algorithm outperformed other algorithms in terms of both the average error and localization accuracy. |
| first_indexed | 2024-03-11T01:08:46Z |
| format | Article |
| id | doaj.art-66ebc9e0653143659061cfb8b4efcefd |
| institution | Directory Open Access Journal |
| issn | 2504-446X |
| language | English |
| last_indexed | 2024-03-11T01:08:46Z |
| publishDate | 2023-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Drones |
| spelling | doaj.art-66ebc9e0653143659061cfb8b4efcefd2023-11-18T19:01:41ZengMDPI AGDrones2504-446X2023-07-017748010.3390/drones7070480Drone-Assisted Fingerprint Localization Based on Kernel Global Locally Preserving ProjectionMengxing Pan0Yunfei Li1Weiqiang Tan2Wengen Gao3School of Electrical Engineering, Anhui Polytechnic University, Wuhu 241000, ChinaSchool of Electrical Engineering, Anhui Polytechnic University, Wuhu 241000, ChinaComputer Science and Cyber Engineering, Guangzhou University, Guangzhou 510006, ChinaSchool of Electrical Engineering, Anhui Polytechnic University, Wuhu 241000, ChinaTo improve the limited number of fixed access points (APs) and the inability to dynamically adjust them in fingerprint localization, this paper attempted to use drones to replace these APs. Drones have higher flexibility and accuracy, can hover in different locations, and can adapt to different scenarios and user needs, thereby improving localization accuracy. When performing fingerprint localization, it is often necessary to consider various factors such as environmental complexity, large-scale raw data collection, and signal strength variation. These factors can lead to high-dimensional and complex nonlinear relationships in location fingerprints, thereby greatly affecting localization accuracy. In order to overcome these problems, this paper proposes a kernel global locally preserving projection (KGLPP) algorithm. The algorithm can reduce the dimensionality of location fingerprint data while preserving its most-important structural information, and it combines global and local information to avoid the problem of reduced information and poor dimensionality reduction effects, which may arise from considering only one. In the process of location estimation, an improved weighted <i>k</i>-nearest neighbor (IWKNN) algorithm is adopted to more accurately estimate the target’s position. Unlike the traditional KNN or WKNN algorithms, the IWKNN algorithm can choose the optimal number of nearest neighbors autonomously, perform location estimation and weight calculation based on the actual situation, and thus, obtain more-accurate location estimation results. The experimental results showed that the algorithm outperformed other algorithms in terms of both the average error and localization accuracy.https://www.mdpi.com/2504-446X/7/7/480droneslocalizationkernel global locally preserving projection (KGLPP)IWKNN |
| spellingShingle | Mengxing Pan Yunfei Li Weiqiang Tan Wengen Gao Drone-Assisted Fingerprint Localization Based on Kernel Global Locally Preserving Projection Drones drones localization kernel global locally preserving projection (KGLPP) IWKNN |
| title | Drone-Assisted Fingerprint Localization Based on Kernel Global Locally Preserving Projection |
| title_full | Drone-Assisted Fingerprint Localization Based on Kernel Global Locally Preserving Projection |
| title_fullStr | Drone-Assisted Fingerprint Localization Based on Kernel Global Locally Preserving Projection |
| title_full_unstemmed | Drone-Assisted Fingerprint Localization Based on Kernel Global Locally Preserving Projection |
| title_short | Drone-Assisted Fingerprint Localization Based on Kernel Global Locally Preserving Projection |
| title_sort | drone assisted fingerprint localization based on kernel global locally preserving projection |
| topic | drones localization kernel global locally preserving projection (KGLPP) IWKNN |
| url | https://www.mdpi.com/2504-446X/7/7/480 |
| work_keys_str_mv | AT mengxingpan droneassistedfingerprintlocalizationbasedonkernelgloballocallypreservingprojection AT yunfeili droneassistedfingerprintlocalizationbasedonkernelgloballocallypreservingprojection AT weiqiangtan droneassistedfingerprintlocalizationbasedonkernelgloballocallypreservingprojection AT wengengao droneassistedfingerprintlocalizationbasedonkernelgloballocallypreservingprojection |