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.

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.

An optimal measurement method for spatial distribution of radio frequency identification multi-tag based on image analysis and PSO

2019 ◽  
Vol 41 (12) ◽  
pp. 3331-3339
Author(s):  
Xiaolei Yu ◽  
Yujun Zhou ◽  
Zhenlu Liu ◽  
Zhimin Zhao
Keyword(s):  
Neural Network ◽  
Image Analysis ◽  
Radio Frequency ◽  
Radio Frequency Identification ◽  
Template Matching ◽  
Detection System ◽  
Optimization Method ◽  
Frequency Identification ◽  
Edge Detection Method ◽  
Reading Distance

In this paper, a multi-tag optimization method based on image analysis and particle swarm optimization (PSO) neural network is proposed to verify the effect of radio frequency identification (RFID) multi-tag distribution on the performance of the system. A RFID tag detection system is proposed with two charge coupled device (CCD). This system can automatically focus on the tag according to its position, so it can obtain the image information more accurately by template matching and edge detection method. Therefore, the spatial structure of multi-tag and the corresponding reading distance can be obtained for training. Because of its excellent performance in multi-objective optimization, the PSO neural network is used to train and predict multi-tag distribution at the maximum reading distance. Compared with other neural networks, PSO is more accurate and its uptime is shorter for RFID multi-tag analysis.

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 RILS: RFID indoor localization system using mobile readers

2018 ◽  
Vol 14 (4) ◽  
pp. 155014771877128 ◽  
Author(s):  
Jinkai Liu ◽  
Yanqing Qiu ◽  
Kezhao Yin ◽  
Wentong Dong ◽  
Jiaqing Luo
Keyword(s):  
Radio Frequency ◽  
Radio Frequency Identification ◽  
Large Scale ◽  
Indoor Localization ◽  
Scheduling Algorithm ◽  
Localization Accuracy ◽  
Localization System ◽  
Continuous Scanning ◽  
Reference Tags ◽  
Frequency Identification

The radio frequency identification technology was given greater interest as it is widely used for identification and localization in the cognitive radio sensor networks. While radio frequency identification–based indoor localization is attractive, the need for a large-scale and high-density deployment of readers and reference tags is costly. Using mobile readers mounted on guide rails, we design and implement an RFID indoor localization system, which requires neither reference tags nor received signal strength indicator functions, for stock-taking and searching in warehouse operations. In particular, we install two guide rails, which can allow a reader to move horizontally or vertically, on the ceiling of a warehouse or workshop. We then propose a continuous scanning algorithm to improve the accuracy for locating a single tagged object and a category-based scheduling algorithm to shorten the time for locating multiple tagged objects. Our primary experimental results show that RFID indoor localization system can achieve high time efficiency and localization accuracy in the indoor localization.

Managing Uncertain Inventory in Supply Chain with Neural Network and Radio Frequency Identification (RFID)

2012 ◽  
pp. 155-170
Author(s):  
CKM Lee ◽  
Ng Wenwei Benjamin ◽  
Shaligram Pokharel
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Supply Chain ◽  
Radio Frequency ◽  
Real Time ◽  
Radio Frequency Identification ◽  
Bullwhip Effect ◽  
Distribution Centre ◽  
Frequency Identification ◽  
Artificial Neural

Demand uncertainty leads to fluctuations in inventory position at each echelon of a supply chain causing bullwhip effect, which can lead to significant cost and loss of efficiency and waste of resources. One of the aspects that can reduce potential bullwhip effect is the sharing of real time information for which the recently mass produced Radio Frequency Identification (RFID) can be of great value. The use of RFID technology can also help in increasing the visibility of the flow of goods and material, keeping track of the location and quantity at each distribution centre and warehouses. This will also help in the periodic and near real time optimization of inventory level of goods and material. The data collected with RFID can be analysed in artificial Neural Network (NN) to forecast the future demand. In this chapter, a framework is proposed by combining RFID with artificial neural network so that lean logistics can be realized in the supply chain.

scholarly journals SP-Loc: A crowdsourcing fingerprint based shop-level indoor localization algorithm integrating shop popularity without the indoor map

2018 ◽  
Vol 14 (11) ◽  
pp. 155014771881563 ◽  
Author(s):  
Jie Wei ◽  
Fang Zhao ◽  
Haiyong Luo
Keyword(s):  
Indoor Localization ◽  
Signal Strength ◽  
Received Signal Strength ◽  
Location Based Services ◽  
Shopping Mall ◽  
Time Stamp ◽  
Localization Algorithm ◽  
Variation Pattern ◽  
Localization Accuracy ◽  
User Location

With the development of indoor localization technology, the location-based services such as product advertising recommendation in the shopping mall attract widespread attention, as precise user location significantly improves the efficiency of advertising push and brings broader profits. However, most of the Wi-Fi-based indoor localization approaches requiring professionals to deploy expensive beacon devices and intensively collect fingerprints in each location grid, which severely limits its extensive promotion. We introduce a zero-cost indoor localization algorithm utilizing crowdsourcing fingerprints to obtain the shop recognition where the user is located. Naturally utilizing the Wi-Fi, GPS, and time-stamp fingerprints collected from the smartphone when user paid as the crowdsourcing fingerprint, we avoid the requirement for indoor map and get rid of both devices cost and manual signal collecting process. Moreover, a shop-level hierarchical indoor localization framework is proposed, and high robustness features based on Wi-Fi sequences variation pattern in the same shop analysis are designed to avoid the received signal strength fluctuations. Besides, we also pay more attention to mine the popularity properties of shops and explore GPS features to improve localization accuracy in the Wi-Fi absence situation effectively. Massive experiments indicate that SP-Loc achieves more than 93% localization accuracy.

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