scholarly journals Inertial measurement unit–aided dual-frequency radio frequency identification localization in line-of-sight and non-line-of-sight hybrid environment

2018 ◽  
Vol 14 (3) ◽  
pp. 155014771876203
Author(s):  
Jie Wu ◽  
Minghua Zhu ◽  
Bo Xiao ◽  
Wei He
Keyword(s):  
Radio Frequency ◽  
Radio Frequency Identification ◽  
Inertial Measurement Unit ◽  
Signal Strength ◽  
Received Signal Strength ◽  
Measurement Unit ◽  
Line Of Sight ◽  
Inertial Measurement ◽  
Frequency Identification ◽  
Non Line Of Sight

The mitigation of non-line-of-sight propagation conditions is one of main challenges in wireless signal–based indoor localization. When radio frequency identification localization technology is applied in applications, the received signal strength fluctuates frequently due to the shade and multipath effect of radio frequency signal, which could result in localization inaccuracy. In particular, when tag carriers are walking in line-of-sight and non-line-of-sight hybrid environment, great attenuation of received signal strength will happen, which would result in great positioning deviation. The article puts forward a dual-frequency radio frequency identification–based indoor localization approach in line-of-sight–non-line-of-sight hybrid environment with the help of inertial measurement unit. Dual-frequency radio frequency identification includes passive radio frequency identification and active radio frequency identification. Passive radio frequency identification is used to assist in determining the tag initial location with passive reader. Active radio frequency identification is used to locate the tag and send the sensor information to active radio frequency identification readers. The proposed method includes three improvements over previous received signal strength–based positioning methods: inertial measurement unit–aided received signal strength filtering, inertial measurement unit–aided line-of-sight/non-line-of-sight distinguishing, and inertial measurement unit–aided line-of-sight/non-line-of-sight environment switching. Also, Cramér–Rao low bound is calculated to prove theoretically that indoor positioning accuracy for the proposed method in line-of-sight and non-line-of-sight mixed environment is higher than position precision using only received signal strength information. Experiments are conducted to show that the proposed method can reduce the mean positioning error to around 3 m without site survey.

scholarly journals Improved Radio Frequency Identification Indoor Localization Method via Radial Basis Function Neural Network

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Dongliang Guo ◽  
Yudong Zhang ◽  
Qiao Xiang ◽  
Zhonghua Li
Keyword(s):  
Neural Network ◽  
Radial Basis Function ◽  
Radio Frequency Identification ◽  
Basis Function ◽  
Indoor Localization ◽  
Signal Strength ◽  
Received Signal Strength ◽  
Radial Basis ◽  
Frequency Identification ◽  
Non Line Of Sight

Indoor localization technique has received much attention in recent years. Many techniques have been developed to solve the problem. Among the recent proposed methods, radio frequency identification (RFID) indoor localization technology has the advantages of low-cost, noncontact, non-line-of-sight, and high precision. This paper proposed two radial basis function (RBF) neural network based indoor localization methods. The RBF neural networks are trained to learn the mapping relationship between received signal strength indication values and position of objects. Traditional method used the received signal strength directly as the input of neural network; we added another input channel by taking the difference of the received signal strength, thus improving the reliability and precision of positioning. Fuzzy clustering is used to determine the center of radial basis function. In order to reduce the impact of signal fading due to non-line-of-sight and multipath transmission in indoor environment, we improved the Gaussian filter to process received signal strength values. The experimental results show that the proposed method outperforms the existing methods as well as improves the reliability and precision of the RFID indoor positioning system.

scholarly journals Indoor localization algorithm based on artificial neural network and radio-frequency identification reference tags

2018 ◽  
Vol 10 (12) ◽  
pp. 168781401880868 ◽  
Author(s):  
Quangang Wen ◽  
Yanchun Liang ◽  
Chunguo Wu ◽  
Adriano Tavares ◽  
Xiaosong Han
Keyword(s):  
Neural Network ◽  
Radio Frequency ◽  
Radio Frequency Identification ◽  
Signal Strength ◽  
Received Signal Strength ◽  
Cumulative Distribution ◽  
Localization Algorithm ◽  
Localization Accuracy ◽  
Frequency Identification ◽  
Internet Of Things Technology

With the development of Internet of Things technology, radio-frequency identification localization methods have been widely applied due to their low cost and ease of deployment. The indoor radio-frequency identification localization algorithm based on received signal strength indication technology is a currently hot topic. Because the received signal strength is highly dependent on environments, the classic algorithms may result in large errors in localization accuracy. This article proposed a new radio-frequency identification localization algorithm, named BP_LANDMARC, by utilizing the back propagation neural network, which is designed to address nonlinear changes in radio-frequency signals. A strategy for selecting different working parameters in variable environments is presented. The evaluation methods of root mean square error and cumulative distribution function are used to compare the proposed algorithm with some existing algorithms. Experimental results show that the proposed algorithm remarkably improves the localization accuracy of both absolute distance and cumulative probability. Moreover, the proposed algorithm performs effectively and efficiently when it is applied to a logistics warehouse management system.

Using Radio Frequency Identification for Indoor Location Identification Based on Power Level, Signal Strength and Fuzzy Method

2011 ◽  
Vol 15 (9) ◽  
pp. 1287-1298
Author(s):  
Rung-Ching Chen ◽  
◽  
Yu-Cheng Lin ◽  
Sheng-Ling Huang ◽  
Qiangfu Zhao
Keyword(s):  
Radio Frequency ◽  
Radio Frequency Identification ◽  
Power Level ◽  
Academic Research ◽  
Signal Strength ◽  
Location Tracking ◽  
Position Location ◽  
Reference Tags ◽  
Frequency Identification ◽  
Set Up

In recent years, there has been a dramatic proliferation of research concerned with Radio Frequency Identification (RFID). RFID technologies are getting considerable attention not only from academic research but also from the applications for enterprise. One of the most important application issues prevailing throughout the last few decades of RFID application research is the indoor position location. Many researchers have used varied technologies to perform the action of indoor position location tracking. In our research, we propose a new method using RFID tags to perform indoor position location tracking. This method uses Received Signal Strength (RSS) to collect signal strengths from reference tags beforehand, and then uses the signal strengths to set up Power Level areas of range by reference tags. Next, using the signal strengths from the reference tags we match signal strengths with track tags. Finally, when the track tags are set up in indoor environments, they can find the position of neighboring reference tags by using the fuzzy set theory and an arithmetic mean to calculate the position location values; with this method we are able to break figures down to track tag position locations. We conducted this experiment to prove that our methodology can provide better accuracy than the LANDMARC system.

scholarly journals Design of a portable radio-frequency-identification reader capable to reading a user memory bank for smart-building energy management

2021 ◽  
Vol 23 (3) ◽  
pp. 1538
Author(s):  
Ajib Setyo Arifin ◽  
M. B. Fathinah Hanun ◽  
Eka Maulana ◽  
I Wayan Mustika ◽  
Fitri Yuli Zulkifli
Keyword(s):  
Radio Frequency ◽  
Energy Management ◽  
Radio Frequency Identification ◽  
Building Energy ◽  
Line Of Sight ◽  
Memory Bank ◽  
Smart Building ◽  
Rfid Reader ◽  
Building Energy Management ◽  
Frequency Identification

Communication is an important factor in smart-building energy management (SBEM). Many communications technologies have been applied to SBEM, including radio-frequency identification (RFID). RFID has been used not only for identification but also for carrying information, which is stored in a user memory bank attached to the tag. To access the user memory bank, an RFID reader should comply with ISO 18000-6C standards. The greatest challenge of RFID-reader technology is its short communication range, which limits the sensing area. To overcome this problem, this paper proposes a portable RFID reader built to an ISO 18000-6C standard to extend the sensing area due to its moveability. The reader is designed using low-cost devices widely available on the market for ease of duplication and assembly by researchers, educators, and startups. The proposed RFID reader can read passive tags with distances up to 12 and 5.5 m for line-of-sight (LOS) and non-line-of-sight (NLOS) communication, respectively. The minimum received-signal-strength indicators (RSSIs) for LOS and NLOS are found to be −63.75 and −59.66 dBm, respectively. These results are comparable with those of non-portable RFID readers on the market.

Non-Line-of-Sight Identification and Mitigation Using Received Signal Strength

2015 ◽  
Vol 14 (3) ◽  
pp. 1689-1702 ◽  
Author(s):  
Zhuoling Xiao ◽  
Hongkai Wen ◽  
Andrew Markham ◽  
Niki Trigoni ◽  
Phil Blunsom ◽  
...  
Keyword(s):  
Signal Strength ◽  
Received Signal Strength ◽  
Line Of Sight ◽  
Non Line Of Sight

scholarly journals Design and Build Setup Mode Microcontroller-Based in Cars

2021 ◽  
Vol 11 (1) ◽  
pp. 50-54
Author(s):  
Alfan Tamamy ◽  
Koesmarijanto Koesmarijanto ◽  
Ridho Hendra Yoga Perdana
Keyword(s):  
Remote Control ◽  
Signal Strength ◽  
Received Signal Strength ◽  
Line Of Sight ◽  
Infra Red ◽  
Motorized Vehicles ◽  
Bluetooth Communication ◽  
Thick Glass ◽  
Beam Range ◽  
Non Line Of Sight

People want a vehicle that is ready to use without having to wait for long or in the sense that the performance of an activity can run efficiently. With a remote control that can control the vehicle remotely, activities in setup mode will be more efficient. In general, remote control is used in motorized vehicles using Infra-red or Bluetooth communication with communication distance 60meters. So we need LoRa module that has longer beam range. The purpose of research is to design remote control and receiver that can control the vehicle such as opening vehicle door lock,  activating the AC, to the starter mode on the car contact with a wider range of sending and receiving information. The results of research indicate that LoRa module has received signal strength value (RSSI) of -65dBm when LOS (line of sight) at distance 10meters and RSSI of -66dBm at non-LOS (non-line of sight) at the same distance. SNR of 9.25dB when LOS and SNR of 6.0dB when non-LOS at distance 10meters. The research results of sending and receiving remote control data have a maximum distance when non-LOS with obstacles 5mm thick glass and 20 cars is 100 meters with a received signal strength of -112dBm. It can be concluded that for non-LOS connectivity between the LoRa SX1278 has an effectiveness distance at 50meters with an RSSI value of -99dBm and an SNR of 0.25dB, for a LOS condition it has an effectiveness at distance 50meters with RSSI value of -96dBm and SNR of 9dB.

Wireless Sensor Node with Passive RFID for Indoor Monitoring System

2017 ◽  
Vol 7 (3) ◽  
pp. 1459 ◽  
Author(s):  
Norsaidah Muhamad Nadzir ◽  
M.K.A. Rahim ◽  
F. Zubir ◽  
A. Zabri ◽  
H.A. Majid
Keyword(s):  
Monitoring System ◽  
Radio Frequency Identification ◽  
Monopole Antenna ◽  
Line Of Sight ◽  
Raspberry Pi ◽  
Wireless Sensor Node ◽  
Frequency Identification ◽  
Passive Rfid ◽  
Indoor Monitoring ◽  
Non Line Of Sight

This paper discusses the development of an indoor monitoring system based on passive radio frequency identification (RFID) system and Raspberry Pi 3. There are two algorithms designed for this project where the first is to link the RFID module to the Raspberry Pi 3, and the other one is to send the data obtained to a database over wireless network via UDOO Quad as a secondary router. The result is then displayed on a localhost generated using XAMPP. The objective of this project is to realize a monitoring system that incorporates different systems such as Raspberry Pi 3, UDOO Quad, and also RFID module by designing algorithms using Python and C programming language. Plus, the performance of the system is also analyzed using different type of antennas such as the Raspberry Pi 3 Antenna, monopole antenna, and a Yagi Uda antenna in terms of power received versus distance in both line of sight position and non-line of sight position. Finally, antenna that produces the best performance for line-of-sight (LOS) propagation is Yagi Uda antenna while monopole antenna is better when it comes to non-line-of-sight (NLOS) propagation.

Sign in / Sign up

Export Citation Format

Share Document