Wi-Fi RTT-Based Active Monopulse RADAR for Single Access Point Localization

López-Pastor, José Antonio; Arques-Lara, Pedro; Franco-Penaranda, Juan Jose; García-Sánchez, Antonio-Javier; Gómez‐Tornero, José Luis

doi:10.1109/access.2021.3062085

Cited by 27 publications

(15 citation statements)

References 29 publications

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“…Ma et al [20] proposed a systematic bias removal process for improving the ranging accuracy, and proposed a positioning method based on clustering-based lateration with the generation of weighted concentric circles. Other research aimed to improve the positioning performance by using machine learning [21]- [24] or by combining Wi-Fi RTT-based positioning methods with other methods such as map information [25], [26], RSS-based ranging [27], sensors [28]- [32], radio detection and ranging (RADAR) [33], and radio fusion (ultra-wideband and cellular) [34]. These previous research studies on Wi-Fi RTT-based positioning methods assume that the locations of the APs are known in advance.…”

Section: Positioning Based On Wi-fi Rtt Rangingmentioning

confidence: 99%

Performance Evaluation of Wi-Fi RTT Lateration without Pre-Constructing a Database

Manabe

SABA

2023

IEICE Trans. Fundamentals

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This paper proposes an algorithm for estimating the location of wireless access points (APs) in indoor environments to realize smartphone positioning based on Wi-Fi without pre-constructing a database. The proposed method is designed to overcome the main problem of existing positioning methods requiring the advance construction of a database with coordinates or precise AP location measurements. The proposed algorithm constructs a local coordinate system with the first four APs that are activated in turn, and estimates the AP installation location using Wi-Fi round-trip time (RTT) lateration and the ranging results between the APs. The effectiveness of the proposed algorithm is confirmed by conducting experiments in a real indoor environment consisting of two rooms of different sizes to evaluate the positioning performance of the algorithm. The experimental results showed the proposed algorithm using Wi-Fi RTT lateration delivers high smartphone positioning performance without a pre-constructed database or precise AP location measurements.

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Section: Positioning Based On Wi-fi Rtt Rangingmentioning

confidence: 99%

Performance Evaluation of Wi-Fi RTT Lateration without Pre-Constructing a Database

Manabe

SABA

2023

IEICE Trans. Fundamentals

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“…However, these approaches still require a necessary number of FTMR coordinates to be known and fixed in advance (typically, 3 for two-dimensional and 4 for three-dimensional positioning, respectively). Moreover, in [17], only one single Wi-Fi access point is required; however, it also requires a 2x2 MIMO antenna array to measure the azimuthal bearing angle-associated to the Direction-of-Arrival (AoA). Another recent study [7] proposed a calibration-free positioning system for which the ranging characteristic is trained and predicted using a neural network.…”

Section: Related Workmentioning

confidence: 99%

“…While the playback shows a different path than the reverse path, some of the estimated positions are still unrealistic. Finally, two backward and replay paths describing different directional information are combined using (17) to give the mixture path. The results fit the user's trajectory better than the other two paths, thus yielding better location labels to calibrate the FTM ranging model.…”

Section: B Crowdsourcing Data Collectionmentioning

confidence: 99%

Fusion-Based Smartphone Positioning Using Unsupervised Calibration of Crowdsourced Wi-Fi FTM

Chan

Lai

et al. 2022

IEEE Access

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This paper presents a multi-source fusion smartphone localization solution using Wi-Fi Fine Time Measurement (FTM) and Pedestrian Dead Reckoning (PDR), calibrated via multi-source and unsupervised crowdsourcing. In crowdsourcing phase, user movement within the site utilizes PDR to infer their location, and this location is used to calibrate the FTM data. The multi-layer perceptron (MLP) of the ranging model is suitable for non-line-of-sight (NLOS) reception, and the ranging accuracy is improved by more than 24%. In the positioning phase, the 90 percentile error of the ranging model trained using only crowdsourced data is less than 1.37m, which is 32% smaller than the traditional weighted least squares (WLS) localization error.

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“…Como se ha comentado en nuestro trabajo anterior [2], para la aplicación de la función monopulso apropiada a la distancia del dispositivo IoT que se desea localizar, es necesario conocer previamente esta distancia, con lo que el número de aplicaciones de este tipo de sistemas es limitado. Con el fin de solventar dicho hándicap se desarrolló este novedoso trabajo [3], publicado en la revista IEEE Access en el año 2021. En él se combina la estimación de DoA mediante la función monopulso de señales WiFi basada en amplitud con el estándar 802.11mc Fine Time Measurement (FTM) Round Trip Time (RTT) que nos permite calcular la distancia al dispositivo.…”

Section: Conclusiones Y Contribuciónunclassified

“…En los trabajos anteriores [1]- [3] se ha empleado el mismo sniffer HAD con antenas de panel directivas para la generación del sistema de localización. Como se comentaba en dichos trabajos, mediante los diagramas  y  se obtiene la función monopulso que solamente es unívoca desde -30° hasta 30°.…”

Section: Breve Introducciónunclassified

Nuevos sistemas de Localización INDoor de dispositivos IoT (LINDIOT)

Pastor¹

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En la primera parte de la tesis se ha trabajado en los sistemas de localización pasivos, concretamente en sistemas de localización mediante la técnica conocida como radar monopulso empleando el estándar WiFi 802.11. Posteriormente se trabajó en la técnica radar monopulso en campo cercano y, a continuación, se combinó con sistemas de ranging basados en el estándar 802.11mc. Mediante la combinación de ambas tecnologías se generó un sistema de localización empleando un único punto de acceso (AP). Por último, el trabajo final de este bloque empleó antenas de haz con escaneo en frecuencia, técnicas de channel-hopping y el algoritmo MUSIC para ampliar el FoV de los dispositivos pasivos implementados en el trabajo anteriore. En la segunda parte de la tesis se ha trabajado en la implementación de un IPS (Indoor Positioning System) de smartphones para grandes superficies, empleando únicamente los APs que ya se encuentran instalados y con una precisión zonal a nivel tienda/pasillo. El IPS es una de las piezas fundamentales de un mecanismo mayor conocido como Sistema de Marketing Contextual. Junto con la descripción detallada del IPS, se explican todos los componentes e intercambios de información entre los diferentes bloques que forman el Sistema de Marketing Contextual.

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Wi-Fi RTT-Based Active Monopulse RADAR for Single Access Point Localization

Cited by 27 publications

References 29 publications

Performance Evaluation of Wi-Fi RTT Lateration without Pre-Constructing a Database

Performance Evaluation of Wi-Fi RTT Lateration without Pre-Constructing a Database

Fusion-Based Smartphone Positioning Using Unsupervised Calibration of Crowdsourced Wi-Fi FTM

Nuevos sistemas de Localización INDoor de dispositivos IoT (LINDIOT)

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