Robust High Resolution Time of Arrival Estimation for Indoor WLAN Ranging

Makki, Ahmed; Siddig, Abubakr; Bleakley, Chris J.

doi:10.1109/tim.2017.2708478

Cited by 29 publications

(11 citation statements)

References 22 publications

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“…LPS and GNSS are categorized through the physical property measured for providing target location: time [10], power [11], phase [12], angle [13], frequency [14], or combinations of these methodologies [15] [16].…”

Section: Introductionmentioning

confidence: 99%

Comparative Performance Analysis of Time Local Positioning Architectures in NLOS Urban Scenarios

et al. 2020

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Section: Introductionmentioning

confidence: 99%

Comparative Performance Analysis of Time Local Positioning Architectures in NLOS Urban Scenarios

et al. 2020

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“…The bandwidth increases from 20 MHz up to 160 MHz, combined with the multiple-input-multiple-output (MIMO) technology, has ignited a rapid standardization effort of a fine-timing measurement (FTM) protocol [1], [2]. This has facilitated the development of accurate, time-delay-based Wi-Fi indoor positioning and navigation systems [5]- [14]. FTM is a point-topoint (P2P) single-user protocol, which includes an exchange of multiple message frames between an initiating Wi-Fi station (ISTA) and a responding station (RSTA).…”

Section: Introductionmentioning

confidence: 99%

Scalable Wi-Fi Client Self-Positioning Using Cooperative FTM-Sensors

Banin

Bar-Shalom

Dvorecki

et al. 2019

IEEE Trans. Instrum. Meas.

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This paper presents a protocol that enables an unlimited number of Wi-Fi users to position themselves within a meter-level accuracy and navigate indoors using time-delay-based Wi-Fi measurements. The proposed protocol, called collaborative time of arrival, is broadcast-based and relies on cooperation between the network sensors that support IEEE 802.11 finetiming measurements (FTMs) capabilities, which are enabled in state-of-the-art Wi-Fi chipsets. The clients can estimate and track their position by passively listening to timing measurements that are exchanged between the FTM-sensors. The passive nature of the clients' operation enables them to maintain their privacy by not exposing their presence to the network. This paper outlines the principles of the protocol and the mathematical background of the position estimation algorithms. Both theoretical analysis of the expected positioning accuracy, as well as real-life system performance examples, are provided. The protocol's performance analysis is based on a publicly available database of real network measurements.

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“…Kietlinski-Zaleski [10] presented a novel algorithm for TOA positioning using only two receivers, which is possible by exploiting reflections from a set of known flat reflectors. Makki [11] proposed a novel robust high-resolution TOA estimation method for IEEE 802.11 g/n range estimation in indoor environments. Furthermore, Zhang [12] presented a three-dimensional positioning method based on the Chan algorithm, which transforms the nonlinear location equation into linear form by using initialization estimation coming from two-step weighed least square, then calculates the location estimation.…”

Section: Introductionmentioning

confidence: 99%

Improved 3-D Indoor Positioning Based on Particle Swarm Optimization and the Chan Method

Chen

Shi

et al. 2018

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Time of arrival (TOA) measurement is a promising method for target positioning based on a set of nodes with known positions, with high accuracy and low computational complexity. However, most positioning methods based on TOA (such as least squares estimation, maximum-likelihood, and Chan, etc.) cannot provide desirable accuracy while maintaining high computational efficiency in the case of a non-line of sight (NLOS) path between base stations and user terminals. Therefore, in this paper, we proposed a creative 3-D positioning system based on particle swarm optimization (PSO) and an improved Chan algorithm to greatly improve the positioning accuracy while decreasing the computation time. In the system, PSO is used to estimate the initial location of the target, which can effectively eliminate the NLOS error. Based on the initial location, the improved Chan algorithm performs iterative computations quickly to obtain the final exact location of the target. In addition, the proposed methods will have computational benefits in dealing with the large-scale base station positioning problems while has highly positioning accuracy and lower computational complexity. The experimental results demonstrated that our algorithm has the best time efficiency and good practicability among stat-of-the-art algorithms.

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Robust High Resolution Time of Arrival Estimation for Indoor WLAN Ranging

Cited by 29 publications

References 22 publications

Comparative Performance Analysis of Time Local Positioning Architectures in NLOS Urban Scenarios

Comparative Performance Analysis of Time Local Positioning Architectures in NLOS Urban Scenarios

Scalable Wi-Fi Client Self-Positioning Using Cooperative FTM-Sensors

Improved 3-D Indoor Positioning Based on Particle Swarm Optimization and the Chan Method

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