Indoor Smartphone Localization: A Hybrid WiFi RTT-RSS Ranging Approach

Guo, Guangyi; Chen, Ruizhi; Ye, Feng; Peng, Xuesheng; Liu, Zuoya; Pan, Yuanjin

doi:10.1109/access.2019.2957753

Cited by 104 publications

(89 citation statements)

References 61 publications

(55 reference statements)

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“…However, the exact time delay caused by the responder must be known, which is difficult to determine. This problem was solved with introducing the IEEE 802.11mc standard, which makes it possible to determine the turnaround time with sufficient precision [ 13 , 14 , 68 , 69 ]. The operational principle is as follows: the smartphone scans the APs in the surroundings and recognizes which of them are RTT-capable.…”

Section: Common Mathematical Modelsmentioning

confidence: 99%

See 1 more Smart Citation

Fundamental Concepts and Evolution of Wi-Fi User Localization: An Overview Based on Different Case Studies

Retscher

2020

Sensors

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Indoor positioning poses a number of challenges, especially in large and complex buildings. Several effects, such as signal attenuation, signal fluctuations, interference, and multipath play a decisive role in signal propagation. The severity of each challenge depends on the method and technology adopted to perform user localization. Wi-Fi is a popular method because of its ubiquity with already available public and private infrastructure in many environments and the ability for mobile clients, such as smartphones, to receive these signals. In this contribution, the fundamental concepts and basics and the evolution of Wi-Fi as the most widely used indoor positioning technology are reviewed and demonstrated using four different conducted case studies. Starting from an analysis of the properties of Wi-Fi signals and their propagation, suitable techniques are identified. The mathematical models of location fingerprinting and lateration are consolidated and assessed as well as new technology directions and developments highlighted. Results of the case studies demonstrate the capability of Wi-Fi for continuous user localization also in dynamic environments and kinematic mode where the user walks with a usual step speed. However, to achieve acceptable localization accuracy, calibration of the devices is required to mitigate the variance problems due to the device heterogeneity.

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Section: Common Mathematical Modelsmentioning

confidence: 99%

“…Guo et al [ 68 ] combine RTT and RSS localization. The results show that this hybrid approach achieves a remarkable improvement in positioning accuracy, robustness, and update rate in both static and dynamic tests, including indoor and outdoor environments.…”

Section: Evolution Of Wi-fi Localizationmentioning

confidence: 99%

Fundamental Concepts and Evolution of Wi-Fi User Localization: An Overview Based on Different Case Studies

Retscher

2020

Sensors

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“…FDoA, which is also named DD (Differential Doppler), is a method analogous to TDoA for estimating the location of a radio emitter based on observations from other points according to different Doppler shift observations of the emitter at different locations [ 34 , 35 ]. Very similar to ToA is RTT (Round Trip Time) method, in which a base station also sends a signal to a mobile device and waits for a response signal [ 36 , 37 , 38 ].…”

Section: Introductionmentioning

confidence: 99%

Method for Remote Determination of Object Coordinates in Space Based on Exact Analytical Solution of Hyperbolic Equations

Kuptsov

Badenko

Ivanov

et al. 2020

Sensors

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Accurate remote determination of the object coordinates in 3D space is one of the main questions in many applications. In one of the most popular methods, such determination of the location of an object uses the measurement by receiving an electromagnetic signal transmitted by several spatially distributed base stations (BS). The main problem is that it is necessary to reduce errors and computation time. To overcome these difficulties, an analytical method for determining the position of an object based on the analysis of time difference of arrival (TDoA) of signals from the transmitter of the object to the receivers of the BS is proposed. One of the main advantages of this method is that it is possible to eliminate the ambiguity in determining the coordinates of the object in space and to increase the accuracy of determining the coordinates when the TDoA measurement between base stations fluctuates. Applications for autonomous automotive vehicles and spacebased positioning systems are analyzed. The results obtained show that the proposed algorithm has an accuracy of determining coordinates several times higher than the method of linearization of hyperbolic equations and is less sensitive to TDoA fluctuations at base stations.

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“…Guo [30] analyzed the distribution of the RTT ranging error and constructed a ranging error model based on Gaussian distribution by fitting. However, it was very difficult to recognize the low-precision range measurement due to the limited positioning information, especially when only the measuring distances were known.…”

Section: Introductionmentioning

confidence: 99%

WiFi RTT Indoor Positioning Method Based on Gaussian Process Regression for Harsh Environments

Cao

Wang

et al. 2020

IEEE Access

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A novel two-way ranging approach was introduced into the Wireless Fidelity (WiFi) standard, and its ranging accuracy reached one meter in a low multipath environment. However, in harsh environments due to multipath or non-line of sight (NLOS), the range measurement based on the WiFi round trip time (RTT) usually has low accuracy and cannot maintain the one-meter accuracy. Thus, this paper proposes an indoor positioning method based on Gaussian process regression (GPR) for harsh environments. There are two stages in the proposed method: construction of a positioning model and location estimation. In the model construction stage, based on known positions of access points (APs), we can determine the position coordinates of some ground points and the reference distances between them and the APs, and the offline ranging difference fingerprints can be generated by the reference distances, which means that there is no need to collect data. Gaussian process regression (GPR) utilizes offline ranging difference fingerprints based on the reference distance to establish a positioning model, and the particle swarm optimization (PSO) algorithm is employed to estimate the GPR hyperparameters. In the location estimation stage, the gathered actual range measurements generate the online ranging difference fingerprint, which is the input data of the positioning model. The output of the model is the estimated position of the smartphone. Experimental results show that the mean errors (MEs) of the proposed method and Least Squares (LS) algorithm are 1.097 and 3.484 meters, respectively, in a harsh environment, and the positioning accuracy of the proposed method improved by 68.5% compared with the LS algorithm.

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Indoor Smartphone Localization: A Hybrid WiFi RTT-RSS Ranging Approach

Cited by 104 publications

References 61 publications

Fundamental Concepts and Evolution of Wi-Fi User Localization: An Overview Based on Different Case Studies

Fundamental Concepts and Evolution of Wi-Fi User Localization: An Overview Based on Different Case Studies

Method for Remote Determination of Object Coordinates in Space Based on Exact Analytical Solution of Hyperbolic Equations

WiFi RTT Indoor Positioning Method Based on Gaussian Process Regression for Harsh Environments

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