Enhanced PDR-BLE Compensation Mechanism Based on HMM and AWCLA for Improving Indoor Localization

Abstract

This paper presents an enhanced PDR-BLE compensation mechanism for improving indoor localization, which is considerably resilient against variant uncertainties. The proposed method of ePDR-BLE compensation mechanism (EPBCM) takes advantage of the non-requirement of linearization of the system around its current state in an unscented Kalman filter (UKF) and Kalman filter (KF) in smoothing of received signal strength indicator (RSSI) values. In this paper, a fusion of conflicting information and the activity detection approach of an object in an indoor environment contemplates varying magnitude of accelerometer values based on the hidden Markov model (HMM). On the estimated orientation, the proposed approach remunerates the inadvertent body acceleration and magnetic distortion sensor data. Moreover, EPBCM can precisely calculate the velocity and position by reducing the position drift, which gives rise to a fault in zero-velocity and heading error. The developed EPBCM localization algorithm using Bluetooth low energy beacons (BLE) was applied and analyzed in an indoor environment. The experiments conducted in an indoor scenario shows the results of various activities performed by the object and achieves better orientation estimation, zero velocity measurements, and high position accuracy than other methods in the literature.

Cost-effective filtering of unreliable proximity detection results based on BLE RSSI and IMU readings using smartphones

“…The accuracy of RSSI calculations can be improved by calibration, use of dedicated devices and analysis of radio signal propagation. This approach however entails high hardware costs and requires preparation of the environment, as demonstrated in 18 .…”

“…To increase accuracy, localization and positioning solutions are sometimes augmented with data from additional sensors (e.g. 18 – 20 ). In 18 , a complex hybrid localization algorithm is described, consisting of pose and velocity estimation based on activity detection and accelerometer readings and using RSSI signal strength.…”

“… 18 – 20 ). In 18 , a complex hybrid localization algorithm is described, consisting of pose and velocity estimation based on activity detection and accelerometer readings and using RSSI signal strength. Despite the precision indicated in the paper, which is higher than the other techniques, the method remains computationally complex and scaling it to a sufficiently large number of users remains a problem.…”

Indoor Positioning System Based on Bluetooth Low Energy Technology and a Nature-Inspired Optimization Algorithm

“…Localization techniques based on RSSI usually face great positioning errors or difficult parameter settings, which can be reduced successfully by combining them [30]. Since many new smartphones already have built-in Bluetooth capabilities, several authors are experimenting with those devices [31], which reduces the need for additional hardware.…”

An Overview of Indoor Localization System for Human Activity Recognition (HAR) in Healthcare

“…To improve localization prediction subject to uncertainties due to the variables at play, Jamil et al [ 72 ] implemented an accurate compensation mechanism on a combined PDR and BLE positioning system. The hidden Markov model was used for recognition of activities.…”