Impact of antenna mutual coupling on WiFi positioning and angle of arrival estimation

Korogodin, Ilya V.; Dneprov, Vladimir V.

doi:10.1109/mwent.2018.8337288

Cited by 10 publications

(4 citation statements)

References 9 publications

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“…The authors in [15] study the impact of WiFi antennas mutual coupling to AOA estimations and proved, through simulation and experiments, that the coupling may cause an offset of approximately 5-10 degrees on AOA estimations in COTS devices. Beamforming has also been used in [16] to increase the range of operation and reduce transmitted power in IoT applications.…”

Section: Angle/direction Of Arrivalmentioning

confidence: 98%

“…The simulation scenario consists of a square-shaped area of 30 m × 30 m, with a single access point placed in the center, i.e., at (15,15). A one-meter-side grid has been drawn on the simulation surface.…”

Section: Simulation Scenariomentioning

confidence: 99%

“…RMSE of the passive station when the reference station is placed (a) at (1,1); (b) at(14,14). AP fixed at(15,15), and sample-set of length seven.…”

mentioning

confidence: 99%

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Passive Round-Trip-Time Positioning in Dense IEEE 802.11 Networks

Martín-Escalona

Zola

2020

Electronics

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The search for a unique and globally available location solution has attracted researchers for a long time. However, a solution for indoor scenarios, where high accuracy is needed, and Global Positioning System (GPS) is not available, has not been found yet. Despite the number of proposals in the literature, some require too long a calibration time for constructing the fingerprinting map, some rely on the periodic broadcast of positioning information that may downgrade the data communication channel, while others require specific hardware components that are not expected to be carried on commercial off-the-shelf (COTS) wireless devices. The scalability of the location solution is another key parameter for next-generation internet of things (IoT) and 5G scenarios. A passive solution for indoor positioning of WiFi devices is first introduced here, which merges a time-difference of arrival (TDOA) algorithm with the novel fine time measurements (FTM) introduced in IEEE 802.11mc. A proof of concept of the WiFi passive TDOA algorithm is detailed in this paper, together with a thorough discussion on the requirements of the proposed algorithm.

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Section: Angle/direction Of Arrivalmentioning

confidence: 98%

Section: Simulation Scenariomentioning

confidence: 99%

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Passive Round-Trip-Time Positioning in Dense IEEE 802.11 Networks

Martín-Escalona

Zola

2020

Electronics

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“…Due to the influence of synchronization errors between devices and the propagation delay of the signal, there will be a certain delay lag in the CIR in the actual data obtained, and there are differences in the delay lag in the CIR obtained from different data packets [30]. According to previous related research [31], the maximum propagation delay of wireless signals indoors is less than 500ns and signals with a delay of more than 500ns are generally reflected signals from objects farther away from the transceiver, or signals that reach the receiver after multiple reflections and refractions in the channel. These multipath signals are meaningless for normal communication and human activity recognition, and they will cause random CSI jitter after superposition.…”

Section: Data Pre-processingmentioning

confidence: 99%

Temporal-Frequency Attention-Based Human Activity Recognition Using Commercial WiFi Devices

et al. 2020

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Human activity recognition has been growing for decades in a variety of technological disciplines. However, in the existing WiFi-based human activity recognition systems, there are the following problems: Firstly, in the processing of channel state information (CSI) data, mainly for the removal of noise in the superimposed signal, there is no effective removal of useless multipath signals; Secondly, the data segmentation algorithm based on the empirical threshold requires manual adjustment of the threshold in different environments, resulting in poor robustness and unstable segmentation; Thirdly, simple learning classification is applied without specific design for CSI data structure and sufficiently abstracting information features. In this paper, a device-free human activity recognition system with a temporalfrequency attention mechanism is proposed, which can be deployed on commercial WiFi devices to identify human's daily activities. Firstly, the multipath signal affected by the channel change is extracted by using the difference of the propagation delay of different multipath, thereby eliminating the delay and invalid multipath signals that have undergone multiple reflections and refractions. Secondly, a neural network model based on attention mechanism is proposed, which assigns different weights to different characteristics and sequences by imitating the human brain to dedicate more attention to important information. Then, the long short-term memory (LSTM) model is used to learn the correlation features of different dimensions to realize human activity recognition. Finally, the system performance is evaluated in different environments, and the experimental results show that our syetem holds a better performance in both line-of-sight (LOS) and non-line-of-sight (NLOS) than the existing human activity recognition systems.

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