Wi-Fi Indoor Localization System Research Articles

Ada-LT IP: Functional Discriminant Analysis of Feature Extraction for Adaptive Long-Term Wi-Fi Indoor Localization in Evolving Environments.

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.

Graph Neural Network-Based WiFi Indoor Localization System With Access Point Selection

Graph Neural Network-Based WiFi Indoor Localization System With Access Point Selection

Performance Analysis of Wi-Fi Indoor Localization with Channel State Information

Recently proposed Wi-Fi indoor localization systems utilizing channel state information (CSI) derived from the received signal achieve admirable accuracy. This paper presents an information-theoretical analysis in the received waveform level to explore the fundamental limits of the approach. In particular, we perform frequency domain Cram$\acute{e}$e´r-Rao bound (CRB) analysis for location estimation with CSI. Our analysis resolves the high-rank Fisher information matrix challenge, and establishes intrinsic connection between parameters to be estimated for localization and the received waveform information observable with the CSI retrieving toolkit. We also analyze the influence of the asynchronization between the transmitter and the receiver to the performance bound of the CSI approach. Moreover, we shed light on the insight into the design of the CSI localization systems. In particular, we show that the CSI approach presents varying performance for localizing targets in different distances and directions with respect to the CSI retrieving anchor point (AP), and the geometric distribution of AP antennas could fundamentally influence the localization performance. Comprehensive experimental results are demonstrated to validate our theoretical analysis.

Comprehensive Investigation on Principle Component Large-Scale Wi-Fi Indoor Localization.

The smartphone market is rapidly spreading, coupled with several services and applications. Some of these services require the knowledge of the exact location of their handsets. The Global Positioning System (GPS) suffers from accuracy deterioration and outages in indoor environments. The Wi-Fi Fingerprinting approach has been widely used in indoor positioning systems. In this paper, Principal Component Analysis (PCA) is utilized to improve the performance and to reduce the computation complexity of the Wi-Fi indoor localization systems based on a machine learning approach. The experimental setup and performance of the proposed method were tested in real indoor environments at a large-scale environment of 960 m2 to analyze the performance of different machine learning approaches. The results show that the performance of the proposed method outperforms conventional indoor localization techniques based on machine learning techniques.

Unambiguous Association of Crowd-Sourced Radio Maps to Floor Plans for Indoor Localization

In many survey-free Wi-Fi indoor localization systems, including the Adaptive indoor Wi-Fi Positioning System (AWPS) we proposed earlier, there is a need to associate unlabeled measurements to a floor plan. In this paper, we address the problem of how to associate a topological radio graph generated by crowd-sourced RSS measurements to an isomorphic Euclidean graph representing the physical space which may come from a geographic information system (GIS) or through automatic image analysis of a paper floor plan. We introduce the concept of Minimum Symmetric Structures (MSS) and Co-rooted Congruent Structures (COCS) as new ways of characterizing automorphism which prevent unambiguous association, and present a structure analysis algorithm for detecting these structures. Then, we derive rules on the number and locations of markers, or RSS measurements with location labels, needed for resolving the automorphism. Applying the analysis proposed in this paper to hypothetical floor plans as well as floor plans used in AWPS and other existing systems, we will demonstrate that often very few location labels are needed in a SLAM system.

Continuous Space Estimation: Increasing WiFi-Based Indoor Localization Resolution without Increasing the Site-Survey Effort.

Although much research has taken place in WiFi indoor localization systems, their accuracy can still be improved. When designing this kind of system, fingerprint-based methods are a common choice. The problem with fingerprint-based methods comes with the need of site surveying the environment, which is effort consuming. In this work, we propose an approach, based on support vector regression, to estimate the received signal strength at non-site-surveyed positions of the environment. Experiments, performed in a real environment, show that the proposed method could be used to improve the resolution of fingerprint-based indoor WiFi localization systems without increasing the site survey effort.