Platform and Algorithm Development for a RFID-Based Indoor Positioning System

Abstract

In recent years, developing Indoor Positioning System (IPS) has become an attractive research topic due to the increasing demands on Location-Based Service (LBS) in indoor environment. Several advantages of Radio Frequency Identification (RFID) Technology, such as anti-interference, small, light and portable size of RFID tags, and its unique identification of different objects, make it superior to other wireless communication technologies for indoor positioning. However, certain drawbacks of existing RFID-based IPSs, such as high cost of RFID readers and active tags, as well as heavy dependence on the density of reference tags to provide the LBS, largely limit the application of RFID-based IPS. In order to overcome these drawbacks, we develop a cost-efficient RFID-based IPS by using cheaper active RFID tags and sensors. Furthermore, we also proposed three localization algorithms: Weighted Path Loss (WPL), Extreme Learning Machine (ELM) and integrated WPL-ELM. WPL is a centralized model-based approach which does not require any reference tags and provides accurate location estimation of the target effectively. ELM is a machine learning fingerprinting-based localization algorithm which can provide higher localization accuracy than other existing fingerprinting-based approaches. The integrated WPL-ELM approach combines the fast estimation of WPL and the high localization accuracy of ELM. Based on the experimental results, this integrated approach provides a higher localization efficiency and accuracy than existing approaches, e.g., the LANDMARC approach and the support vector machine for regression (SVR) approach.

A Review of Wearable Technologies for Elderly Care that Can Accurately Track Indoor Position, Recognize Physical Activities and Monitor Vital Signs in Real Time

“…Therefore, a supplementary approach must be adopted to achieve a robust and precise indoor positioning and tracking system. For example, the authors of [28,44,45,47,129,130,131,132,133,134,135] combined PDR with other approaches to improve localization accuracy (e.g., GPS, ultrasound ranging, active RFID, WiFi signatures, and Chirp Spread Spectrum (CSS) radio beacons). The accuracy of these approaches can be greatly improved compared with stand-alone PDR systems, with errors reaching below 1.7 m. Hence, in this review, a scheme fusing a PDR system with a magnetic indoor positioning technique is recommended.…”

Mobile Robot Indoor Positioning System Based on K-ELM

“…Over the past decade, numerous universities and research institutes have conducted in-depth research on indoor positioning technology and proposed many indoor positioning technologies such as UWB [4], RFID [5], Zigbee [6], and Bluetooth [7]. Nevertheless, all of the indoor positioning technologies require the installation of additional infrastructure.…”

Robust and accurate localization algorithms for indoor positioning and navigation

“…In order to evaluate the performance of WPL, extensive experiments have been conducted on our RFID based IPS [17]. The test-bed is set up in the Internet of Before the performance evaluation, we define the reasonable range of received signal strength the sensor can pick up from the tags firstly.…”

“…It is noteworthy that one of the machine learning algorithms called extreme learning machine (ELM) has attracted a lot of attention in recent years due to its fast learning speed and easy implementation [96,97]. In Chapter 3, our proposed RFID-based IPS, which adopts ELM as the localization algorithm, is able to deliver a better performance in terms of both the efficiency and localization accuracy [17][18][19].…”

“…In this chapter, we propose three localization algorithms, namely Weighted Path Loss (WPL) [18], Extreme Learning Machine (ELM) [17] and integrated WPL-ELM [19], that can be directly utilized for all RF based IPS. As presented in Section 3.1, WPL is a path loss model-based approach which does not require offline site survey procedure and provides a reasonably accurate location estimation of the target effectively.…”