Low-Cost Indoor Localization System Combining Multilateration and Kalman Filter

Sestrem, Leonardo; Rayel, Ohara Kerusauskas; Leitão, Paulo

doi:10.1109/isie45552.2021.9576353

Cited by 10 publications

(8 citation statements)

References 19 publications

(37 reference statements)

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“…The resulting root mean square error (RMSE) was 1.79 m for the localization of 15 different positions. Based on these results, the proposed application demonstrates a lower error rate within a dimension cell 12.5 m × 6 m compared to state-of-the-art methods presented in [17,18]. These methods, which use trilateration or multilateration, have higher errors using cells of 5 m × 5 m and 9.77 m × 13.45 m, respectively.…”

Section: Estimation Of Exponent Path-loss Modelmentioning

confidence: 77%

“…In this context, numerous studies have been conducted to evaluate the performance of different technologies faced with different challenges. Table 1 shows the performance of different technologies in different scenarios according to [17][18][19][20][21][22][23]. In [17], the authors conducted a comparative analysis between trilateration and min-max approaches.…”

Section: Related Workmentioning

confidence: 99%

“…This specific operating frequency is known to cover distances of approximately 1-3 m. The multipath interference generates unreliable RSSI values and causes an average error of 3.6 m, which is higher compared to the tested area. In [18], a practical implementation of a Bluetooth Low Energy (BLE)-based localization system was introduced. The used system integrates multilateration and Kalman filtering techniques to provide a low-cost solution while maintaining high positional accuracy.…”

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

A Self-Localization Algorithm for Mobile Targets in Indoor Wireless Sensor Networks Using Wake-Up Media Access Control Protocol

Souissi,

Sahnoun,

Baazaoui

et al. 2024

Sensors

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Indoor localization of a mobile target represents a prominent application within wireless sensor network (WSN), showcasing significant values and scientific interest. Interference, obstacles, and energy consumption are critical challenges for indoor applications and battery replacements. A proposed tracking system deals with several factors such as latency, energy consumption, and accuracy presenting an innovative solution for the mobile localization application. In this paper, a novel algorithm introduces a self-localization algorithm for mobile targets using the wake-up media access control (MAC) protocol. The developed tracking application is based on the trilateration technique with received signal strength indication (RSSI) measurements. Simulations are implemented in the OMNeT++ discrete event simulator using the C++ programming language, and the RSSI values introduced are based on real indoor measurements. In addition, a determination approach for finding the optimal parameters of RSSI is assigned to implement for the simulation parameters. Simulation results show a significant reduction in power consumption and exceptional accuracy, with an average error of 1.91 m in 90% of cases. This method allows the optimization of overall energy consumption, which consumes only 2.69% during the localization of 100 different positions.

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Section: Estimation Of Exponent Path-loss Modelmentioning

confidence: 77%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

A Self-Localization Algorithm for Mobile Targets in Indoor Wireless Sensor Networks Using Wake-Up Media Access Control Protocol

Souissi,

Sahnoun,

Baazaoui

et al. 2024

Sensors

View full text Add to dashboard Cite

show abstract

“…Pseudo-range multilateration, also known as hyperbolic positioning, is the process of locating an object by accurately calculating the difference in arrival time (TDOA) of a signal emitted by the object to three or more receivers [ 35 ]. In this case, the time of emission of the traveling signal is unknown.…”

Section: Localization Methodsmentioning

confidence: 99%

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

Bibbò

Carotenuto

Corte

2022

Sensors

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The number of older people needing healthcare is a growing global phenomenon. The assistance in long-term care comprises a complex of medical, nursing, rehabilitation, and social assistance services. The cost is substantial, but technology can help reduce spending by ensuring efficient health services and improving the quality of life. Advances in artificial intelligence, wireless communication systems, and nanotechnology allow the creation of intelligent home care systems avoiding hospitalization with evident cost containment. They are capable of ensuring functions of recognition of activities, monitoring of vital functions, and tracking. However, it is essential to also have information on location in order to be able to promptly intervene in case of unforeseen events or assist people in carrying out activities in order to avoid incorrect behavior. In addition, the automatic detection of physical activities performed by human subjects is identified as human activity recognition (HAR). This work presents an overview of the positioning system as part of an integrated HAR system. Lastly, this study contains each technology’s concepts, features, accuracy, advantages, and limitations. With this work, we want to highlight the relationship between HAR and the indoor positioning system (IPS), which is poorly documented in the literature.

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“…3. The functioning of the multi-lateration method is however subject to the condition that RSSI values are available at each AP (at least 3) at every instant of time, and is measured with a high level of accuracy [18,19,23]. If the data acquired does not fulfill such preconditions, the multi-lateration method would either not be applicable or yield unsatisfactory results.…”

Section: Location Prediction Using Multilateration (Hyperbolic Positi...mentioning

confidence: 99%

Utilizing machine learning algorithms for localization using RSSI values of wireless LAN

Ganguly¹,

Nayak²,

Irene³

et al. 2022

ITU J-FET

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With the development of new technologies, there has been an upsurge in the demand for precise localization in both outdoor and indoor environments. While a Global Positioning System (GPS) provides sufficient positioning precision in outdoor settings, its accuracy declines in indoor scenarios, necessitating the development of novel positioning approaches that function accurately both indoors and outdoors. The use of various Wireless Local Area Network (WLAN) parameters for localization has been conceptualized. In this study, we attempt to do localization using machine learning methods on WLAN Received Signal Strength Indicator (WLAN RSSI) measurements. We compare the performance of multiple machine learning algorithms on the data set to see which can be used to design efficient future localization systems. The proposed study has achieved second place for the problem statement "ITU-ML5G-PS-016: Location estimation using RSSI of wireless LAN" in AI/ML in 5G Challenge 2021 organized by the International Telecommunication Union.

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Low-Cost Indoor Localization System Combining Multilateration and Kalman Filter

Cited by 10 publications

References 19 publications

A Self-Localization Algorithm for Mobile Targets in Indoor Wireless Sensor Networks Using Wake-Up Media Access Control Protocol

A Self-Localization Algorithm for Mobile Targets in Indoor Wireless Sensor Networks Using Wake-Up Media Access Control Protocol

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

Utilizing machine learning algorithms for localization using RSSI values of wireless LAN

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