A Look at the Recent Wireless Positioning Techniques With a Focus on Algorithms for Moving Receivers

Tahat, Ashraf; Kaddoum, Georges; Yousefi, Siamak; Valaee, Shahrokh; Gagnon, François

doi:10.1109/access.2016.2606486

Cited by 112 publications

(70 citation statements)

References 198 publications

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“…The received signal strength (RSS) has been used successfully for active localization of wireless devices using WiFi fingerprinting techniques as summarized in [5]. The RSS has also been used as a metric for passive tracking of mobile objects [6].…”

Section: A Techniques Based On Wi-fi Signal Powermentioning

confidence: 99%

A Survey on Behavior Recognition Using WiFi Channel State Information

et al. 2017

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In this article, we present a survey of recent advances in passive human behaviour recognition in indoor areas using the channel state information (CSI) of commercial WiFi systems. Movement of human body causes a change in the wireless signal reflections, which results in variations in the CSI. By analyzing the data streams of CSIs for different activities and comparing them against stored models, human behaviour can be recognized. This is done by extracting features from CSI data streams and using machine learning techniques to build models and classifiers. The techniques from the literature that are presented herein have great performances, however, instead of the machine learning techniques employed in these works, we propose to use deep learning techniques such as long-short term memory (LSTM) recurrent neural network (RNN), and show the improved performance. We also discuss about different challenges such as environment change, frame rate selection, and multi-user scenario, and suggest possible directions for future work.

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Section: A Techniques Based On Wi-fi Signal Powermentioning

confidence: 99%

A Survey on Behavior Recognition Using WiFi Channel State Information

et al. 2017

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“…While TOA-and FOA-based navigation approaches are well-studied in the literature [13], [14], applying such approaches to cellular CDMA base transceiver stations (BTSs) or LTE eNodeBs requires perfect synchronization assumptions [15], [16]. However, cellular CDMA and LTE networks are not perfectly synchronized, and their protocols recommend synchronization of CDMA BTSs and LTE eNodeBs to within 3 microseconds from GPS time [17], [18].…”

Section: Introductionmentioning

confidence: 99%

Opportunistic UAV Navigation With Carrier Phase Measurements From Asynchronous Cellular Signals

Khalife

Kassas

2020

IEEE Trans. Aerosp. Electron. Syst.

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This paper presents a framework for opportunistic unmanned aerial vehicle (UAV) navigation by exploiting carrier phase measurements from ambient cellular signals of opportunity. In the proposed framework, the cellular base transceiver stations (BTSs) are not assumed to be synchronous. A complete framework that employs an extended Kalman filter (EKF) is presented, including filter initialization and process and measurement noise covariance selection. The EKF estimates the position and velocity of the UAV as well as the differences between the UAV-mounted receiver and each of the BTSs' clock bias and clock drift. The observability of the estimation framework is analyzed and the boundedness of the EKF's errors is studied. A lower bound for the EKF estimation error covariance is derived, and it is shown that the covariance remains bounded. Monte Carlo simulations are conducted to study the effect of the number of BTSs, the initial UAV speed, and the receiver's oscillator quality, on the estimation performance. Two sets of experimental results are presented demonstrating UAVs navigating exclusively with cellular carrier phase measurements via the developed framework, achieving a total position root mean-squared error of 2.94 m and 5.99 m for UAV trajectories of 2.6 km and 2.9 km, respectively.

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“…The GSM service providers introduced various methods to estimate locations more accurately . Recently, the location information used in various applications such as in the community services, logistics, mobile marketing, and location‐aware communication . Currently, 5G technology adopted five different technology to improve the localization problem .…”

Section: Introductionmentioning

confidence: 99%

A probabilistic‐based approach for direction‐of‐arrival estimation and localization of multiple sources

Abdelbari¹,

Bilgehan²

2020

Int J Communication

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Summary This article contributes to science at two points. The first contribution is at a point of introducing a novel direction‐of‐arrival (DOA) estimation method which based on subspaces methods called Probabilistic Estimation of Several Signals (PRESS). The PRESS method provides higher resolution and DOA accuracy than current models. Second contribution of the article is at a point of localizing the unknown signal source. The process of localization achieved by using DOA information for the first time. The importance of localization exists in a large area of engineering applications. The aim is to determine the location of multiple sources by using PRESS with minimum effort of computation. We used the maximum probabilistic process in this study. Initially, all the signals are collected by the array of sensors and accurately identified using the proposed algorithm. The receiver at the best in test estimates the source location using only the knowledge of the geographical latitude and longitude values of the array of sensors. Several test points with an accurately calculated angle of arrival enable us to draw linear lines towards the transmitter. The transmitter location can be accurately identified with the line of interceptions. Simulation and numerical results show the outstanding performance of both the DOA estimation method and transmitter localization approach compared with many classical and new DOA estimation methods. The PRESS localization method first tested at 19°, 26°, and 35° with an signal‐to‐noise ratio (SNR) value of ‐5 dB. The PRESS method produced results with an extremely low bias of 0 and 0.00080°. The simulation tests are repeated and produced results with zero bias, which give the exact location of the unknown source.

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A Look at the Recent Wireless Positioning Techniques With a Focus on Algorithms for Moving Receivers

Cited by 112 publications

References 198 publications

A Survey on Behavior Recognition Using WiFi Channel State Information

A Survey on Behavior Recognition Using WiFi Channel State Information

Opportunistic UAV Navigation With Carrier Phase Measurements From Asynchronous Cellular Signals

A probabilistic‐based approach for direction‐of‐arrival estimation and localization of multiple sources

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