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Article
Publication date: 23 May 2018

Wei Zhang, Xianghong Hua, Kegen Yu, Weining Qiu, Shoujian Zhang and Xiaoxing He

This paper aims to introduce the weighted squared Euclidean distance between points in signal space, to improve the performance of the Wi-Fi indoor positioning. Nowadays…

Abstract

Purpose

This paper aims to introduce the weighted squared Euclidean distance between points in signal space, to improve the performance of the Wi-Fi indoor positioning. Nowadays, the received signal strength-based Wi-Fi indoor positioning, a low-cost indoor positioning approach, has attracted a significant attention from both academia and industry.

Design/methodology/approach

The local principal gradient direction is introduced and used to define the weighting function and an average algorithm based on k-means algorithm is used to estimate the local principal gradient direction of each access point. Then, correlation distance is used in the new method to find the k nearest calibration points. The weighted squared Euclidean distance between the nearest calibration point and target point is calculated and used to estimate the position of target point.

Findings

Experiments are conducted and the results indicate that the proposed Wi-Fi indoor positioning approach considerably outperforms the weighted k nearest neighbor method. The new method also outperforms support vector regression and extreme learning machine algorithms in the absence of sufficient fingerprints.

Research limitations/implications

Weighted k nearest neighbor approach, support vector regression algorithm and extreme learning machine algorithm are the three classic strategies for location determination using Wi-Fi fingerprinting. However, weighted k nearest neighbor suffers from dramatic performance degradation in the presence of multipath signal attenuation and environmental changes. More fingerprints are required for support vector regression algorithm to ensure the desirable performance; and labeling Wi-Fi fingerprints is labor-intensive. The performance of extreme learning machine algorithm may not be stable.

Practical implications

The new weighted squared Euclidean distance-based Wi-Fi indoor positioning strategy can improve the performance of Wi-Fi indoor positioning system.

Social implications

The received signal strength-based effective Wi-Fi indoor positioning system can substitute for global positioning system that does not work indoors. This effective and low-cost positioning approach would be promising for many indoor-based location services.

Originality/value

A novel Wi-Fi indoor positioning strategy based on the weighted squared Euclidean distance is proposed in this paper to improve the performance of the Wi-Fi indoor positioning, and the local principal gradient direction is introduced and used to define the weighting function.

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Article
Publication date: 16 January 2017

Wei Zhang, Xianghong Hua, Kegen Yu, Weining Qiu, Xin Chang, Bang Wu and Xijiang Chen

Nowadays, WiFi indoor positioning based on received signal strength (RSS) becomes a research hotspot due to its low cost and ease of deployment characteristics. To further…

Abstract

Purpose

Nowadays, WiFi indoor positioning based on received signal strength (RSS) becomes a research hotspot due to its low cost and ease of deployment characteristics. To further improve the performance of WiFi indoor positioning based on RSS, this paper aims to propose a novel position estimation strategy which is called radius-based domain clustering (RDC). This domain clustering technology aims to avoid the issue of access point (AP) selection.

Design/methodology/approach

The proposed positioning approach uses each individual AP of all available APs to estimate the position of target point. Then, according to circular error probable, the authors search the decision domain which has the 50 per cent of the intermediate position estimates and minimize the radius of a circle via a RDC algorithm. The final estimate of the position of target point is obtained by averaging intermediate position estimates in the decision domain.

Findings

Experiments are conducted, and comparison between the different position estimation strategies demonstrates that the new method has a better location estimation accuracy and reliability.

Research limitations/implications

Weighted k nearest neighbor approach and Naive Bayes Classifier method are two classic position estimation strategies for location determination using WiFi fingerprinting. Both of the two strategies are affected by AP selection strategies and inappropriate selection of APs may degrade positioning performance considerably.

Practical implications

The RDC positioning approach can improve the performance of WiFi indoor positioning, and the issue of AP selection and related drawbacks is avoided.

Social implications

The RSS-based effective WiFi indoor positioning system can makes up for the indoor positioning weaknesses of global navigation satellite system. Many indoor location-based services can be encouraged with the effective and low-cost positioning technology.

Originality/value

A novel position estimation strategy is introduced to avoid the AP selection problem in RSS-based WiFi indoor positioning technology, and the domain clustering technology is proposed to obtain a better accuracy and reliability.

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