Wi-Fi RTT based indoor positioning with dynamic weighted multidimensional scaling

Yan, Shuo; Luo, Haiyong; Zhao, Fang; Shao, Wenhua; Li, Zhaohui; Crivello, Antonino

doi:10.1109/ipin.2019.8911783

Cited by 20 publications

(4 citation statements)

References 11 publications

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“…A discussion on range-based and range-free methods and the challenges in indoor localization is also presented. Another work on ranging and positioning based on WiFi sensing, is presented in [27]. The authors manage to achieve an accuracy within 2.5m in 80% cases.…”

Section: B Related Workmentioning

confidence: 99%

Crowd Forecasting Based on WiFi Sensors and LSTM Neural Networks

Singh

Determe

Horlin

et al. 2020

IEEE Trans. Instrum. Meas.

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To ensure effective management and security in large scale public events, it is imperative for the event organizers to be aware of potentially critical crowd densities. This paper, therefore, presents a solution to the above problem in terms of WiFi based crowd counting and LSTM neural network based forecasting. Monitoring of an actual event organized in Brussels has been described, wherein crowd counts are obtained using WiFi sensors in a privacy-preserved manner. The time-stamped crowd counts are used to develop univariate time-series, which are in-turn utilized for forecasting. Five different LSTM models are utilized for crowd time-series forecasting and analyzed for their suitability. A random walk model is used as reference for performance assessment. Among different LSTM models, Convolutional LSTM delivered the best performance. Overall results and analysis show that the developed system is suitable for crowd monitoring.

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Section: B Related Workmentioning

confidence: 99%

Crowd Forecasting Based on WiFi Sensors and LSTM Neural Networks

Singh

Determe

Horlin

et al. 2020

IEEE Trans. Instrum. Meas.

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“…Visual evaluation, though more indicative of the userperceived experience, is not complete without numerical evaluation. We compare EKF-SH not only with EKF-RW, but also with other EKFs using step and heading motion models, namely (20) and (22), tagged ''Ref. 1'' and ''Ref.…”

Section: B Step and Heading Ekfmentioning

confidence: 99%

“…[21] compares the ranging accuracy between WiFi RTT and UWB. [22] proposes a positioning algorithm based on multi-dimensional scaling, by converting measurements of distances between wireless nodes, specifically RTT, into two-dimensional coordinates. [23] proposes an RTT-based fingerprinting method based on Gaussian process regression, using particle swarm optimization to tune the model's hyperparameters.…”

Section: Introductionmentioning

confidence: 99%

WhereArtThou: A WiFi-RTT-Based Indoor Positioning System

Jurdi,

Chen,

Zhu

et al. 2024

IEEE Access

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Accurate indoor positioning is critical to a variety of use cases including tracking, proximity, mapping, and navigation. Existing positioning methods are either inaccurate (e.g. RSSI-based ranging), impractical (e.g. fingerprinting), or uncommon (e.g. Ultra-Wide Band/UWB). WiFi Round-Trip Time (RTT) marries the ubiquity of WiFi infrastructure with the accuracy of such ranging mechanisms as those used in UWB. We present WhereArtThou, a commercial-grade, plug-and-play indoor positioning solution (IPS) based on WiFi RTT. Our base algorithm uses an extended Kalman filter with a random walk motion model (EKF-RW) which relies solely on RTT distance measurements. We propose two EKF components to enhance positioning accuracy. First, as distance measurements can further be fused with inertial sensor readings, we propose a step-and-heading-based filter (EKF-SH) when such readings are available. We devise a method to fit the non-Gaussian step error with a Gaussian random variable to remain within the computationallyefficient Kalman filtering framework. Second, we define a distance-dependent measurement model to match the true statistics of the measurement noise and approach optimal position estimation. Moreover, we measure the gain from the proposed enhancements not only through the almost-exclusively-used error metric in the indoor positioning literature, the Euclidean distance and its variations, but also through metrics commonly used in satellite and maritime navigation, the cross-track and along-track errors, in addition to a set of metrics of our own definition. Finally, we show that RTT is highly susceptible to the human body holding the device measuring it, and we case-study the impact of human-body blockage on positioning and ranging errors. We test our algorithms on over 18 hours of walking data collected on different devices, in different locations, and with different users, and we observe that the EKF-RW and EKF-SH achieve 90th percentile distance errors of 1.65 m and 1.45 m, and 90th percentile cross-track errors of 0.85 m and 1.55 m, making our solution primed for commercial deployment. INDEX TERMS Indoor positioning, indoor localization, wireless positioning, device-based positioning, range-based positioning, WiFi round-trip time (RTT), fine-timing measurement (FTM), ranging, Bayesian filter, Kalman filter, pedestrian dead reckoning (PDR), inertial measurement unit (IMU), ground truth.

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“…In the literature, there are many studies focused on indoor positioning [1][2][3][4][5][6][7][8][9][10][11][12][13][14]. In a study, a high-performance positioning process was performed with a method based on the round-trip time (RTT) of WiFi signals [1]. Here, researchers have developed two different position estimation approaches, which they call coarse and fine.…”

Section: Introductionmentioning

confidence: 99%

Weighted Visibility Graph Based WiFi Indoor Positioning Method Using Heuristic Optimization

Altundoğan,

Karaköse

2024

Turkish Journal of Science and Technology

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With the widespread use of wireless communication technologies and IoT applications, researchers are developing approaches that utilize WiFi signals for indoor location determination. In this study, indoor positioning process based on heuristic optimization-based methods was performed by creating weighted visibility matrices of access points based on WiFi signal strength (RSSI) values. In the proposed method, the PSO and GA approaches determine the position of the mobile user using a common fitness function based on the visibility weight matrices. The proposed method has been tested on a virtual scenario where position ranges based on RSSI ranges are determined. Both heuristic optimization methods are compared according to different criteria and the positioning process is performed with a maximum error of 3m for the GA based method and a maximum of 1.5m for the PSO based method.

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Wi-Fi RTT based indoor positioning with dynamic weighted multidimensional scaling

Cited by 20 publications

References 11 publications

Crowd Forecasting Based on WiFi Sensors and LSTM Neural Networks

Crowd Forecasting Based on WiFi Sensors and LSTM Neural Networks

WhereArtThou: A WiFi-RTT-Based Indoor Positioning System

Weighted Visibility Graph Based WiFi Indoor Positioning Method Using Heuristic Optimization

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