Research Article A Clustering-Based Approach for Improving the Accuracy of UWB Sensor-Based Indoor Positioning System Taner Arsan and Mohammed Muwafaq Noori Hameez Computer Engineering Department, Kadir Has University, Istanbul 34083, Turkey Correspondence should be addressed to Taner Arsan; arsan@khas.edu.tr Received 6 July 2019; Revised 20 August 2019; Accepted 3 September 2019; Published 26 September 2019 Academic Editor: Jinglan Zhang Copyright © 2019 Taner Arsan and Mohammed Muwafaq Noori Hameez. is is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. ere 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 pen- etration. is 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. is 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. is 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 al- gorithm with the Kalman filter in which the Kalman-filtered UWB-measured test points are used as an input for the proposed system. 1. Introduction With the expansion of information technology, indoor positioning technology has developed rapidly. Positioning methods are mainly divided into two categories: the lo- cation fingerprint positioning method and the trilateration algorithm [1]. e need for high-accuracy indoor posi- tioning is a very important issue. Determining the location of patients in the hospital, locating workers in a large office, and also 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 sig- nals. ese solutions manage to provide information re- lated 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. e lateration derivations determine the location of the target by measuring the distances of this target from a number of reference points, instead of directly measuring the distance. e time difference of arrival (TDoA), the time of arrival (ToA), or received signal strengths (RSS) are usually measured; and the distance is obtained by calcu- lating the attenuation of the transmitted signal strength or, in another case, by multiplying the travel time and the velocity of the radio signal. e round trip time of flight (RToF) method is also used for range estimation purposes in some systems. However, angulation helps us to locate a target by calculating the angles relative to the reference points in the angle of arrival (AoA) method [4, 5]. Many positioning systems have different architectures, configu- rations, accuracies, and reliabilities to determine the po- sition of objects or people. Some of the indoor positioning systems are GPS, infrared, Wi-Fi, RFID, BLE Beacon, ul- trasonic location-based systems, and UWB [6, 7]. UWB signals have an extremely large bandwidth, more than 500 MHz. UWB transmitters allow better power efficiency due to its low consumption of power, compared to other Hindawi Mobile Information Systems Volume 2019, Article ID 6372073, 13 pages https://doi.org/10.1155/2019/6372073