ArticleSensors (Basel, Switzerland)2022
A Semi-Supervised 3D Indoor Localization Using Multi-Kernel Learning for WiFi Networks.
Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Wi-Fi-Based Indoor Localization and Navigation: A Robot-Aided Hybrid Deep Learning Approach.Sensors (Basel, Switzerland) · 2023Article
- Improved CNN-Based Indoor Localization by Using RGB Images and DBSCAN Algorithm.Sensors (Basel, Switzerland) · 2022Article
- Crowdsourcing-Based Indoor Semantic Map Construction and Localization Using Graph Optimization.Sensors (Basel, Switzerland) · 2022Article
- Cost-Effective Fitting Model for Indoor Positioning Systems Based on Bluetooth Low Energy.Sensors (Basel, Switzerland) · 2022Article
- RSS/TDoA-Based Source Localization in Microwave UWB Sensors Networks Using Two Anchor Nodes.Sensors (Basel, Switzerland) · 2022Article
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Authors and funding
3 authors.
Funding
Abstract
Indoor localization is an important issue for indoor location-based services. As opposed to the other indoor localization approaches, the radio frequency (RF) based approaches are low-energy solutions with simple implementation. The kernel learning has been used for the RF-based indoor localization in 2D environment. However, the kernel learning has not been used in 3D environment. Hence, this paper proposes a multi-kernel learning scheme for 3D indoor localization. Based on the signals collected in the area of interest, the WiFi signals with better quality and closer to the user are selected so as to reduce the multipath effect and the external interference. Through the construction of multi-kernel, the localization accuracy can be improved as opposed to the localization based on the single kernel. We build multiple kernels to get the user's location by collecting wireless received signal strengths (RSS) and signal-to-noise ratios (SNR). The kernel learning maps data to high dimension space and uses the optimization process to find the surface where the data are mapped. By multi-kernel training, the surface is fine-tuned and eventually converges to form the location database during the mapping process. The proposed localization scheme is verified by the real RSS and SNR collected from multiple wireless access points (AP) in a building. The experimental results verify that the proposed multi-kernel learning scheme performs better than the multi-DNN scheme and the existing kernel-based localization schemes in terms of localization accuracy and error in 3D indoor environment.
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Registered trials
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