Drone-Assisted Fingerprint Localization Based on Kernel Global Locally Preserving Projection

• Published in: Drones (Basel) Vol. 7; no. 7; p. 480
• Main Authors: Pan, Mengxing, Li, Yunfei, Tan, Weiqiang, Gao, Wengen
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01-07-2023
• Subjects: Accuracy; Algorithms; Broadband; Communication; Complexity; Data collection; Drones; Energy consumption; Fingerprints; Internet of Things; IWKNN; kernel global locally preserving projection (KGLPP); Kernels; Localization; Mathematical analysis; Methods; Signal strength; Unmanned aerial vehicles; Wireless access points; Iran
• ISSN: 2504-446X; 2504-446X
• DOI: 10.3390/drones7070480

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 k-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.