Improving Accuracy and Reliability of Bluetooth Low-Energy-Based Localization Systems Using Proximity Sensors

Abstract

One of the functionalities which are desired in Ambient and Assisted Living systems is accurate user localization at their living place. One of the best-suited solutions for this purpose from the cost and energy efficiency points of view are Bluetooth Low Energy (BLE)-based localization systems. Unfortunately, their localization accuracy is typically around several meters and might not be sufficient for detection of abnormal situations in elderly persons behavior. In this paper, a concept of a hybrid positioning system combining typical BLE-based infrastructure and proximity sensors is presented. The proximity sensors act a supporting role by additionally covering vital places, where higher localization accuracy is needed. The results from both parts are fused using two types of hybrid algorithms. The paper contains results of simulation and experimental studies. During the experiment, an exemplary proximity sensor VL53L1X has been tested and its basic properties modeled for use in the proposed algorithms. The results of the study have shown that employing proximity sensors can significantly improve localization accuracy in places of interest.

Automatic radio map creation in a fingerprinting‐based BLE/UWB localisation system

“…The obtained results were also compared with conventional non‐hybrid methods: TDOA‐based extended Kalman filter (UWB) described in Section 3.1 and extended Kalman filter utilising BLE power measurements (BLE). The implementation of BLE‐based EKF was similar to its BLE counterpart (the complete description can be found in [33].…”

Wireless Sensor Network Localization Using AoA Measurements With Two-Step Error Variance-Weighted Least Squares

“…In the first step, the terminal location is provisionally localized in the 3D space using the least squares. Let the orthogonal vectors c 2i for the relative position vector x − a i be as in (11).…”

“…Assuming that |n φi | and |n ψi | 1, and employing geometric relations and additive theorems for trigonometric functions, (12) can be transformed into (15), where r i denotes the Euclidean distance between the anchor and the terminal in the x and y planes, and d i denotes the Euclidean distance between the anchor and the terminal in the 3D space. By giving the orthogonal vectors, c 1i , as in (7), and c 2i , as in (11), the errors can be expressed in terms of the Euclidean distance and the standard deviation alone.…”

“…Among the rangebased methods, received signal strength (RSS), time of flight (ToF), time of arrival (TOA), and time difference of arrival (TDoA) are the common measurements employed to estimate location [7]- [10]. Among the methods that do not require range information, proximity detection, fingerprint matching, and angle-of-arrival (AoA) measurements are common [11]- [16]. AoA-based localization methods are highly accurate, and various ways have been studied [14]- [16].…”

An Efficient Single-Anchor Localization Method Using Ultra-Wide Bandwidth Systems

“…Radio frequency (RF) signals are widely used for indoor localization, such as Bluetooth, Wi-Fi, and radio-frequency identification (RFID)-based localization systems [9][10][11][12]. However, it is challenging for these RF technologies to achieve sub-meter level accuracy and robust localization.…”