Indoor Localization Method Based on Regional Division with IFCM

Li, Junhuai; Gao, Xixi; Hu, Zhiyong; Cao, Ting; Yu, Lei

doi:10.3390/electronics8050559

Cited by 16 publications

(10 citation statements)

References 46 publications

(46 reference statements)

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“…Clustering algorithms have been widely used in fingerprintbased positioning. Luo and Fu [31], Li et al [40], and Zhong et al [41] proposed some clustering algorithms such as attraction propagation clustering (APC), fuzzy C-means (FCM), and Kmeans, which can narrow the positioning area and improve results. Fingerprints are not fully mined in traditional clustering algorithms.…”

Section: Related Workmentioning

confidence: 99%

Indoor localisation algorithm based on MSIA and FCSO

Liu

Zhu

et al. 2020

IET Communications

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Section: Related Workmentioning

confidence: 99%

Indoor localisation algorithm based on MSIA and FCSO

Liu

Zhu

et al. 2020

IET Communications

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“…Then, in the online phase, an object’s signal collected from a location in real time is compared with the fingerprints in the RFM, to solve for the location of the object [18,30]. In this phase, fingerprinting is based on classification algorithms and methods, such as neural networks [11,12], decision trees [23], k-nearest neighbors [31], support vector machines [31] and random forests [32], which predict the object’s current location based on the fingerprint database [33,34]. Since fingerprinting relies on signal strength, the problems that may occur are related to signal variations deriving from communication issues, such as fading, interference or even from environmental factors.…”

Section: Related Workmentioning

confidence: 99%

“…Thus, they are more effective in terms of the cost of gathering data [46]. Other studies have applied fingerprinting [33,47] and classifiers to achieve effective and more accurate Wi-Fi-based indoor positioning [11,48].…”

Section: Related Workmentioning

confidence: 99%

An Ensemble Filter for Indoor Positioning in a Retail Store Using Bluetooth Low Energy Beacons

Stavrou

Bardaki

Papakyriakopoulos

et al. 2019

Sensors

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This paper has developed and deployed a Bluetooth Low Energy (BLE) beacon-based indoor positioning system in a two-floor retail store. The ultimate purpose of this study was to compare the different indoor positioning techniques towards achieving efficient position determination of moving customers in the retail store. The innovation of this research lies in its context (the retail store) and the fact that this is not a laboratory, controlled experiment. Retail stores are challenging environments with multiple sources of noise (e.g., shoppers’ moving) that impede indoor localization. To the best of the authors’ knowledge, this is the first work concerning indoor localization of consumers in a real retail store. This study proposes an ensemble filter with lower absolute mean and root mean squared errors than the random forest. Moreover, the localization error is approximately 2 m, while for the random forest, it is 2.5 m. In retail environments, even a 0.5 m deviation is significant because consumers may be positioned in front of different store shelves and, thus, different product categories. The more accurate the consumer localization, the more accurate and rich insights on the customers’ shopping behavior. Consequently, retailers can offer more effective customer location-based services (e.g., personalized offers) and, overall, better consumer localization can improve decision making in retailing.

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“…If the value of K is not set properly, it will have a certain negative impact on the subsequent positioning results. Li et al [10] proposed the improved fuzzy c-means algorithm for regional division in the offline training phase, which introduces the K-means clustering algorithm and between-within proportion (BWP) index to select the optimal initial clustering centre and number of clusters. He et al [11] proposed a regional division method based on a Voronoi graph that is constructed with the initial reference points as the generating points, and the virtual reference points are partitioned into the nearest Voronoi region.…”

Section: Introductionmentioning

confidence: 99%

Research on a Wi-Fi RSSI Calibration Algorithm Based on WOA-BPNN for Indoor Positioning

Yao

et al. 2022

Applied Sciences

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Owing to the heterogeneity of software and hardware in different types of mobile terminals, the received signal strength indication (RSSI) from the same Wi-Fi access point (AP) varies in indoor environments, which can affect the positioning accuracy of fingerprint methods. To solve this problem and consider the nonlinear characteristics of Wi-Fi signal strength propagation and attenuation, we propose a whale optimisation algorithm-back-propagation neural network (WOA-BPNN) model for indoor Wi-Fi RSSI calibration. Firstly, as the selection of the initial parameters of the BPNN model has a considerable impact on the positioning accuracy of the calibration algorithm, we use the WOA to avoid blindly selecting the parameters of the BPNN model. Then, we propose an improved nonlinear convergence factor to balance the searchability of the WOA, which can also help to optimise the calibration algorithm. Moreover, we change the structure of the BPNN model to compare its influence on the calibration effect of the WOA-BPNN calibration algorithm. Secondly, in view of the low positioning accuracy of indoor fingerprint positioning algorithms, we propose a region-adaptive weighted K-nearest neighbour positioning algorithm based on hierarchical clustering. Finally, we effectively combine the two proposed algorithms and compare the results with those of other calibration algorithms such as the linear regression (LR), support vector regression (SVR), BPNN, and genetic algorithm-BPNN (GA-BPNN) calibration algorithms. The test results show that among different mobile terminals, the proposed WOA-BPNN calibration algorithm can increase positioning accuracy (one sigma error) by 41%, 42%, 44% and 36%, on average. The indoor field tests suggest that the proposed methods can effectively reduce the indoor positioning error caused by the heterogeneous differences of software and hardware in different mobile terminals.

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Indoor Localization Method Based on Regional Division with IFCM

Cited by 16 publications

References 46 publications

Indoor localisation algorithm based on MSIA and FCSO

Indoor localisation algorithm based on MSIA and FCSO

An Ensemble Filter for Indoor Positioning in a Retail Store Using Bluetooth Low Energy Beacons

Research on a Wi-Fi RSSI Calibration Algorithm Based on WOA-BPNN for Indoor Positioning

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