ArticleSensors (Basel, Switzerland)2024
Ada-LT IP: Functional Discriminant Analysis of Feature Extraction for Adaptive Long-Term Wi-Fi Indoor Localization in Evolving Environments.
Article in Sensors (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
4 citing papers in PubMed.
- Indoor Localization Algorithm Based on Information Gain Ratio and Affinity Propagation Clustering.Sensors (Basel, Switzerland) · 2026Article
- A Wi-Fi/PDR Fusion Localization Method Based on Genetic Algorithm Global Optimization.Sensors (Basel, Switzerland) · 2025Article
- Indoor Positioning Systems as Critical Infrastructure: An Assessment for Enhanced Location-Based Services.Sensors (Basel, Switzerland) · 2025Review
- Theories and Methods for Indoor Positioning Systems: A Comparative Analysis, Challenges, and Prospective Measures.Sensors (Basel, Switzerland) · 2024Review
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Wi-Fi fingerprint-based indoor localization methods are effective in static environments but encounter challenges in dynamic, real-world scenarios due to evolving fingerprint patterns and feature spaces. This study investigates the temporal variations in signal strength over a 25-month period to enhance adaptive long-term Wi-Fi localization. Key aspects explored include the significance of signal features, the effects of sampling fluctuations, and overall accuracy measured by mean absolute error. Techniques such as mean-based feature selection, principal component analysis (PCA), and functional discriminant analysis (FDA) were employed to analyze signal features. The proposed algorithm, Ada-LT IP, which incorporates data reduction and transfer learning, shows improved accuracy compared to state-of-the-art methods evaluated in the study. Additionally, the study addresses multicollinearity through PCA and covariance analysis, revealing a reduction in computational complexity and enhanced accuracy for the proposed method, thereby providing valuable insights for improving adaptive long-term Wi-Fi indoor localization systems.
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Registered trials
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