Indoor Positioning System Based on the RSSI Using Passive Tags

Murofushi, Rodrigo Hiroshi; Goncalves, Rafael Froede; Sousa, Alexandre Rodrigues de; Tavares, José Jean-Paul Zanlucchi de Souza

doi:10.1109/lars-sbr.2016.61

Cited by 5 publications

(4 citation statements)

References 9 publications

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“…Generally, RFID technology consists of a reader, tags, and a computer [2,26]. RFID technologies are based on an active tag technology [28,127] and passive tag technology [25,124,128,129]. Active tags have a larger detecting range using high power consumption and higher cost, although the passive tags are appropriate for short distance static point location, and only applicable for a small space [38].…”

Section: Rfid Technologymentioning

confidence: 99%

“…Two groups of indoor localization systems are used, namely active localization and passive localization [22]. RFID [23][24][25][26][27][28], UWB [29][30][31], Bluetooth [32][33][34], ZigBee [35][36][37][38], IR [15,17,39], ultrasonic [40][41][42], hybrid systems and the standard of the Institution of Electrical and Electronic Engineering (IEEE) 802.11 WLANs [42] are included in active IPS, in which the person is attached to the tag or device to enable locating the position in a dynamic indoor localization framework. If the person does not carry any tag or device alongside with the trajectory in the location area, then it is defined as a passive indoor localization.…”

Section: Introductionmentioning

confidence: 99%

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Review of Indoor Positioning: Radio Wave Technology

et al. 2020

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The indoor positioning system (IPS) is becoming increasing important in accurately determining the locations of objects by the utilization of micro-electro-mechanical-systems (MEMS) involving smartphone sensors, embedded sources, mapping localizations, and wireless communication networks. Generally, a global positioning system (GPS) may not be effective in servicing the reality of a complex indoor environment, due to the limitations of the line-of-sight (LoS) path from the satellite. Different techniques have been used in indoor localization services (ILSs) in order to solve particular issues, such as multipath environments, the energy inefficiency of long-term battery usage, intensive labour and the resources of offline information collection and the estimation of accumulated positioning errors. Moreover, advanced algorithms, machine learning, and valuable algorithms have given rise to effective ways in determining indoor locations. This paper presents a comprehensive review on the positioning algorithms for indoors, based on advances reported in radio wave, infrared, visible light, sound, and magnetic field technologies. The traditional ranging parameters in addition to advanced parameters such as channel state information (CSI), reference signal received power (RSRP), and reference signal received quality (RSRQ) are also presented for distance estimation in localization systems. In summary, the recent advanced algorithms can offer precise positioning behaviour for an unknown environment in indoor locations.

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Section: Rfid Technologymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Review of Indoor Positioning: Radio Wave Technology

et al. 2020

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“…Although it can be read directly from a commercial WiFi access point at a distance and does not require manual signal collection, multiple WiFi and RFID tags need to be installed at the target location to continuously monitor the signal strength level and send the scanned data to the server. In Murofushi et al, 24 a one-dimensional indoor positioning system based on RSSI power transmission of backscatter signal coupled by RFID tag to the position of mobile vehicle is introduced and compared with the distance measured by ultrasonic sensor; though it has higher positioning accuracy, the antennas are incompatible and need to have a fixed position. In Lohan et al, 25 the RFID and WLAN channel positioning models are compared, and the path loss channel models of WLAN and RFID signals are analyzed.…”

Section: Introductionmentioning

confidence: 99%

LANDMARC indoor positioning algorithm based on density-based spatial clustering of applications with noise–genetic algorithm–radial basis function neural network

Ren

Bao

Zhang

et al. 2020

International Journal of Distributed Sensor Networks

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In recent years, fifth-generation communication technology has begun to experiment successfully. As an indoor positioning technology of the Internet of things, it changes with each passing day and shows great vitality in the development of smart cities. Aiming at the problem that existing radio frequency identification indoor positioning algorithm is prone to environmental interference and poor positioning accuracy, a LANDMARC indoor positioning algorithm based on density-based spatial clustering of applications with noise–genetic algorithm–radial basis function neural network is proposed. In this article, the signal intensity value is processed by Gaussian filter, and the noise points and boundary points are removed by density-based clustering algorithm. The threshold and weight of radial basis function neural network were optimized by genetic algorithm. With less data information, the relationship between the value of label signal strength and position coordinate could be established to improve the positioning accuracy of LANDMARC positioning algorithm. Experimental research shows that the average positioning error of the proposed LANDMARC algorithm based on density-based spatial clustering of applications with noise–genetic algorithm–radial basis function neural network is about 0.9 m, which is 64% lower than the average positioning error of the traditional LANDMARC algorithm and improves the indoor positioning accuracy.

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“…This system enables the transfer of information in an object or group of objects. It is based on cooperating transponders (passive or active) and a reader, stimulating them to operate and read the information sent [49].…”

mentioning

confidence: 99%

The Latest Advances in Wireless Communication in Aviation, Wind Turbines and Bridges

et al. 2022

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Present-day technologies used in SHM (Structural Health Monitoring) systems in many implementations are based on wireless sensor networks (WSN). In the context of the continuous development of these systems, the costs of the elements that form the monitoring system are decreasing. In this situation, the challenge is to select the optimal number of sensors and the network architecture, depending on the wireless system’s other parameters and requirements. It is a challenging task for WSN to provide scalability to cover a large area, fault tolerance, transmission reliability, and energy efficiency when no events are detected. In this article, fundamental issues concerning wireless communication in structural health monitoring systems (SHM) in the context of non-destructive testing sensors (NDT) were presented. Wireless technology developments in several crucial areas were also presented, and these include engineering facilities such as aviation and wind turbine systems as well as bridges and associated engineering facilities.

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Indoor Positioning System Based on the RSSI Using Passive Tags

Cited by 5 publications

References 9 publications

Review of Indoor Positioning: Radio Wave Technology

Review of Indoor Positioning: Radio Wave Technology

LANDMARC indoor positioning algorithm based on density-based spatial clustering of applications with noise–genetic algorithm–radial basis function neural network

The Latest Advances in Wireless Communication in Aviation, Wind Turbines and Bridges

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