Abstract
There are several methods which can be used to locate an object or people in an indoor location. Ultra-wideband (UWB) is a specifically promising indoor positioning technology because of its high accuracy, resistance to interference, and better penetration. This study aims to improve the accuracy of the UWB sensor-based indoor positioning system. To achieve that, the proposed system is trained by using the K-means algorithm with an additional average silhouette method. This helps us to define the optimal number of clusters to be used by the K-means algorithm based on the value of the silhouette coefficient. Fuzzy c-means and mean shift algorithms are added for comparison purposes. This paper also introduces the impact of the Kalman filter while using the measured UWB test points as an input for the Kalman filter in order to obtain a better estimation of the position. As a result, the average localization error is reduced by 43.26% (from 16.3442 cm to 9.2745 cm) when combining the K-means algorithm with the Kalman filter in which the Kalman-filtered UWB-measured test points are used as an input for the proposed system.
Highlights
With the expansion of information technology, indoor positioning technology has developed rapidly
Standalone Clustering Implementation. e proposed system is applicable for K-means, fuzzy c-means (FCM), and mean shift algorithms. e average silhouette method is used in order to define the optimal number of clusters in K-means and FCM algorithms for each test point by varying k from 2 to 6 clusters
The mean shift algorithm has the lowest accuracy (14.4748 cm), when it is compared with K-means and FCM algorithms, despite its advantage. e main advantages of mean shift algorithms stem from the nonparametric nature of the kernel density estimate (KDE); and the user needs to set only one parameter, the bandwidth. is is often more convenient than having to select the number of clusters explicitly or utilizing other methods to define the number of clusters such as the average silhouette or the elbow methods
Summary
With the expansion of information technology, indoor positioning technology has developed rapidly. E need for high-accuracy indoor positioning is a very important issue. Determining the location of patients in the hospital, locating workers in a large office, and people trapped in a burning building are all part of scenarios that require a high accuracy indoor positioning systems. Numerous solutions are presented for location estimation of indoor targets [2, 3]. A large number of these solutions rely on multilateration and triangulation methods by utilizing ultrasound, infrared, and radio signals. Ese solutions manage to provide information related to the location. Triangulation utilizes the properties of triangles to determine the target position. It includes two derivations: first, the lateration, and second, the angulation
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