Abstract
With the development of wireless technology, indoor localization has gained wide attention. The fingerprint localization method is proposed in this paper, which is divided into two phases: offline training and online positioning. In offline training phase, the Improved Fuzzy C-means (IFCM) algorithm is proposed for regional division. The Between-Within Proportion (BWP) index is selected to divide fingerprint database, which can ensure the result of regional division consistent with the building plane structure. Moreover, the Agglomerative Nesting (AGNES) algorithm is applied to accomplish Access Point (AP) optimization. In the online positioning phase, sub-region selection is performed by nearest neighbor algorithm, then the Weighted K-nearest Neighbor (WKNN) algorithm based on Pearson Correlation Coefficient (PCC) is utilized to locate the target point. After the evaluation on the effect of regional division and AP optimization of location precision and time, the experiments show that the average positioning error is 2.53 m and the average computation time of the localization algorithm based on PCC reduced by 94.13%.
Highlights
With the continuous development of wireless communication technology and ubiquitous computing in recent years, as well as the growing demand for Location Based Services (LBS) [1], wireless positioning technology has become more widely used
This paper introduces Pearson Correlation Coefficient (PCC) and Spearman correlation coefficient, and compares the positioning accuracy of Weighted K-nearest Neighbor (WKNN) algorithm based on Euclidean distance, PCC and Spearman correlation coefficient
To reduce the positioning time and improve the positioning accuracy, we proposed a fingerprint localization method based on regional division with Improved Fuzzy C-means (IFCM)
Summary
With the continuous development of wireless communication technology and ubiquitous computing in recent years, as well as the growing demand for Location Based Services (LBS) [1], wireless positioning technology has become more widely used. In the online positioning phase, the RSS information of each AP collected in real time is compared with the existing information in the fingerprint database, and the matching algorithm is used to perform matching calculation to estimate the current location of the user. To solve the problem of huge databases, while still achieving high localization accuracy, this paper proposes an indoor positioning method based on regional division with the Improved Fuzzy. It introduces the K-means clustering algorithm and the Between-Within Proportion (BWP) index to select the optimal initial clustering center and the number of clusters In this way, we can reduce the amount of calculation of the fingerprint matching algorithm and improve the real-time performance of the algorithm.
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