Wi-Fi Radio Map Interpolation with Sparse and Correlated Received Signal Strength Measurements for Indoor Positioning

Kiring, Aroland; Yew, Hoe Tung; Farm, Yan Yan; Chung, Seng Kheau; Wong, Farrah; Chekima, Ali

doi:10.1109/iicaiet49801.2020.9257857

Cited by 8 publications

(4 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…More complex approaches to the RM interpolation can utilise deep learning [41]. The kNN and Inverse Distance Weighting (IDW) algorithms are also capable of approximating missing values in RM [42]. Collection of RMs can be improved by the use of inertial sensors for support in the Wi-Fi RSSI location tracking [43].…”

Section: Related Workmentioning

confidence: 99%

Influence of Measured Radio Map Interpolation on Indoor Positioning Algorithms

et al. 2023

View full text Add to dashboard Cite

Indoor positioning and navigation increasingly has become popular and there are many different approaches, using different technologies. In nearly all of the approaches the locational accuracy depends on signal propagation characteristics of the environment. What makes many of these approaches similar is the requirement of creating a signal propagation Radio Map (RM) by analysing the environment. As this is usually done on a regular grid, the collection of Received Signal Strength Indicator (RSSI) data at every Reference Point (RP) of a RM is a time consuming task. With indoor positioning being in the focus of the research community, the reduction in time required for collection of RMs is very useful as it allows researchers to spend more time with research instead of data collection. In this paper we analyse the options for reducing the time required for the acquisition of RSSI information. We approach this by collecting initial RMs of Wi-Fi signal strength using 5 ESP32 micro controllers working in monitoring mode and placed around our office. We then analyse the influence the approximation of RSSI values in unreachable places has, by using linear interpolation and Gaussian Process Regression (GPR) to find balance between final positioning accuracy, computing complexity, and time requirements for the initial data collection. We conclude that the computational requirements can be significantly lowered, while not affecting the positioning error, by using RM with a single sample per RP generated considering many measurements.

show abstract

Section: Related Workmentioning

confidence: 99%

Influence of Measured Radio Map Interpolation on Indoor Positioning Algorithms

et al. 2023

View full text Add to dashboard Cite

show abstract

“…To interpolate the RSS values for the incomplete radio map, the IDW and KNN algorithms are used, and their performance is compared with each other. The interpolation errors computed from the RMSE between the predicted and actual RSS measurements for both the IDW and KNN algorithms are analyzed with and without the spatial correlation over a variety of sparsity parameters [8].…”

Section: Related Workmentioning

confidence: 99%

Enhanced Radio Map Interpolation Methods Based on Dimensionality Reduction and Clustering

Khoo

Tan

2022

Electronics

View full text Add to dashboard Cite

The received signal strength (RSS) based Wi-Fi fingerprinting method is one of the most potential and easily deployed approaches for a reliable indoor positioning system. However, due to the labor intensive and time-consuming radio map construction process, interpolation is often incorporated. To ensure the interpolated radio map is robust against environmental noise and RSS fluctuations, we propose two novel interpolation methods, termed as DimRed and DimRedClust, for an improved radio map construction. The former performs dimensionality reduction prior to the interpolation while the latter employs both the dimensionality reduction and clustering before interpolating the radio map. For dimensionality reduction, principal component analysis (PCA) or truncated singular value decomposition (TSVD) is adopted to profoundly extract essential features from the RSS data while the K-means algorithm is used to partition the reference points (RPs) into several clusters. Subsequently, the RSS for all virtual points are interpolated via inverse distance weighting (IDW). Numerical results based on the real-world multi-floor multi-building dataset confirm the supremacy of the proposed schemes over the baseline IDW interpolation. Compared to the baseline IDW, the proposed PCA-K-means-IDW, TSVD-K-means-IDW, PCA-IDW, and TSVD-IDW could attain a performance gain in terms of average positioning error of up to 30.17%, 30.93%, 19.33%, and 21.61%, respectively.

show abstract

“…Therefore, one major issue with RSS-based fingerprinting is that offline site surveying is time-consuming, costly, and labour-intensive. Various radio map construction and interpolation techniques have been proposed to virtually build the fingerprints from the limited collected samples to reduce human effort in collecting fingerprints, such as the Kriging [13][14][15], IDW [16,17], and single PLM [18] interpolation techniques. However, the main issue with the traditional Kriging and IDW interpolators is that these methods can only provide accurate performance when deployed in a sufficiently confined deployment area.…”

Section: Introductionmentioning

confidence: 99%

“…In a later publication, Zhao et al [15] (2016) combined the universal kriging interpolation method, KNN, and naive Bayes classifier to improve the Wi-Fi fingerprinting with positioning accuracy of 1.265 m in boundary space. More recent work by Kiring et al [17] (2020) proposed using both KNN and IDW algorithms to interpolate the incomplete Wi-Fi radio map based on sparsely collected labeled training data.…”

Section: Introductionmentioning

confidence: 99%

Robust Fingerprint Construction Based on Multiple Path Loss Model (M-PLM) for Indoor Localization

Yong¹,

Tan²,

Tan³

et al. 2023

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

A robust radio map is essential in implementing a fingerprint-based indoor positioning system (IPS). However, the offline site survey to manually construct the radio map is time-consuming and labour-intensive. Various interpolation techniques have been proposed to infer the virtual fingerprints to reduce the time and effort required for offline site surveys. This paper presents a novel fingerprint interpolator using a multi-path loss model (M-PLM) to create the virtual fingerprints from the collected sample data based on different signal paths from different access points (APs). Based on the historical signal data, the poor signal paths are identified using their standard deviations. The proposed method reduces the positioning errors by smoothing out the wireless signal fluctuations and stabilizing the signals for those poor signal paths. By considering multipath signal propagations from different APs, the inherent noise from these signal paths can be alleviated. Firstly, locations of the signal data with standard deviations higher than the threshold are identified. The new fingerprints are then generated at these locations based on the proposed M-PLM interpolation function to replace the old fingerprints. The proposed technique interpolates virtual fingerprints based on good signal paths with more stable signals to improve the positioning performance. Experimental results show that the proposed scheme enhances the positioning accuracy by up to 44% compared to the conventional interpolation techniques such as the Inverse Distance Weighting, Kriging, and single Path Loss Model. As a result, we can overcome the site survey problems for IPS by building an accurate radio map with more reliable signals to improve indoor positioning performance.

show abstract

Wi-Fi Radio Map Interpolation with Sparse and Correlated Received Signal Strength Measurements for Indoor Positioning

Cited by 8 publications

References 23 publications

Influence of Measured Radio Map Interpolation on Indoor Positioning Algorithms

Influence of Measured Radio Map Interpolation on Indoor Positioning Algorithms

Enhanced Radio Map Interpolation Methods Based on Dimensionality Reduction and Clustering

Robust Fingerprint Construction Based on Multiple Path Loss Model (M-PLM) for Indoor Localization

Contact Info

Product

Resources

About