The Horus WLAN location determination system

Youssef, Moustafa; Agrawala, Ashok K.

doi:10.1145/1067170.1067193

Cited by 1,371 publications

(873 citation statements)

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“…Hence, update rule (30) guarantees that the Hamiltonian H is monotonically decreasing, which proves that the adaptation law (29) eventually leads to the optimal control gain K o .…”

Section: Corollary 1 (Adapting the Gain K)mentioning

confidence: 75%

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Mobile robot trajectory tracking using noisy RSS measurements: An RFID approach

Miah

Gueaieb

2014

ISA Transactions

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Most mobile robot navigation techniques that depend on reference RF beacons rely on approximating line-of-sight (LOS) distances between these beacons and the robot. The approximation of LOS is mostly performed using received signal strength (RSS) measurements of signals propagating between the robot and RF beacons. However, to date, relying on RSS measurements for approximating LOS distance remains a significant challenge. Accurate mapping between RSS measurements and LOS distance is almost impossible to achieve in an indoor reverberant environment. In this paper, we design a partially-observed feedback controller for a differential drive mobile robot (DDMR) where the feedback signal is in the form of noisy RSS measurements emitted from radio frequency identification (RFID) tags placed in the environment. The proposed controller does not require an accurate mapping between the LOS distance and the RSS measurements from RFID tags. In addition, it takes into account the robot's actuator (speed) constraints. Unlike many other previously devised solutions, the proposed control scheme does not require the linearization of the nonlinear DDMR model. The performance of this method is demonstrated through both numerical simulations and real-time experiments.

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“…Hence, update rule (30) guarantees that the Hamiltonian H is monotonically decreasing, which proves that the adaptation law (29) eventually leads to the optimal control gain K o .…”

Section: Corollary 1 (Adapting the Gain K)mentioning

confidence: 75%

“…This is due to the fact that RSS measurements can be obtained relatively effortlessly and inexpensively. In addition, no extra hardware (e.g., ultrasonic or infra-red) is needed for network-centric localization [30]. On the other hand, bearing-based schemes use the direction of arrival (DoA) of a target.…”

Section: Introductionmentioning

confidence: 99%

Mobile robot trajectory tracking using noisy RSS measurements: An RFID approach

Miah

Gueaieb

2014

ISA Transactions

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“…In addition, we provide 95% confidence intervals to our estimation using Bootstrapping method. Compared with other approaches, for example, in [3] and [16], our method is simpler, more systematic and more accurate. Experimental results…”

Section: Chapter 1 Introductionmentioning

confidence: 97%

“…The challenge is how to determine locations accurately and efficiently, especially in indoor environments. Existing indoor approaches utilize different types of signals such as infrared [15], ultrasound [10], and radio frequency (RF) [3,16,11,12,17,13] to estimate locations. Among these approaches, the RF-based ones are the most promising, because they can be easily integrated with existing and widely spread 802.11 infrastructure.…”

Section: Introductionmentioning

confidence: 99%

“…Among these approaches, the RF-based ones are the most promising, because they can be easily integrated with existing and widely spread 802.11 infrastructure. RF-based approaches can be further classified depending on the metrics they measure, such as Triangulation [12], Time of Arrival (ToA) [17], Time Difference of Arrival (TDoA) [13] and Received Signal Strength (RSS) [3,16,11].…”

Section: Introductionmentioning

confidence: 99%

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A Statistical Indoor Localization Method for Supporting Location-based Access Control

et al. 2009

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Semantic wireless localization of WiFi terminals in smart buildings

et al. 2016

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The wireless localization of mobile terminals in indoor scenarios by means of a semantic interpretation of the environment is addressed in this work. A training‐less approach based on the real‐time calibration of a simple path loss model is proposed which combines (i) the received signal strength information measured by the wireless terminal and (ii) the topological features of the localization domain. A customized evolutionary optimization technique has been designed to estimate the optimal target position that fits the complex wireless indoor propagation and the semantic target‐environment relation, as well. The proposed approach is experimentally validated in a real building area where the available WiFi network is opportunistically exploited for data collection. The presented results point out a reduction of the localization error obtained with the introduction of a very simple semantic interpretation of the considered scenario.

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The Horus WLAN location determination system

Cited by 1,371 publications

References 10 publications

Mobile robot trajectory tracking using noisy RSS measurements: An RFID approach

Mobile robot trajectory tracking using noisy RSS measurements: An RFID approach

A Statistical Indoor Localization Method for Supporting Location-based Access Control

Semantic wireless localization of WiFi terminals in smart buildings

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