Neural networks applications for the prediction of propagation path loss in urban environments

Popescu, I.; Nafomita, I.; Constantinou, P.; Kanatas, Αthanasios G.; Moraitis, Nektarios

doi:10.1109/vetecs.2001.944870

Cited by 23 publications

(28 citation statements)

References 8 publications

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“…The neural network is employed to predict the NLOS error [59]; Kalman filters [60] and modified two-stage Kalman filter [61] are used to correct NLOS measurements.…”

Section: Nlos Mitigationmentioning

confidence: 99%

A Survey of Localization in Wireless Sensor Network

Cheng

Zhang

et al. 2012

International Journal of Distributed Sensor Networks

222

153

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Localization is one of the key techniques in wireless sensor network. The location estimation methods can be classified into target/source localization and node self-localization. In target localization, we mainly introduce the energy-based method. Then we investigate the node self-localization methods. Since the widespread adoption of the wireless sensor network, the localization methods are different in various applications. And there are several challenges in some special scenarios. In this paper, we present a comprehensive survey of these challenges: localization in non-line-of-sight, node selection criteria for localization in energy-constrained network, scheduling the sensor node to optimize the tradeoff between localization performance and energy consumption, cooperative node localization, and localization algorithm in heterogeneous network. Finally, we introduce the evaluation criteria for localization in wireless sensor network.

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“…The neural network is employed to predict the NLOS error [59]; Kalman filters [60] and modified two-stage Kalman filter [61] are used to correct NLOS measurements.…”

Section: Nlos Mitigationmentioning

confidence: 99%

A Survey of Localization in Wireless Sensor Network

Cheng

Zhang

et al. 2012

International Journal of Distributed Sensor Networks

222

153

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“…If the starting data This work was supported in part by the by the European Union, under the projects MonAmI and EasyLine+ and by the Spanish MCYT under AmbienNET project (TIN2006-15617-C03-02) 978-1-4244-2644-7/08/$25.00 ©2008 IEEE are not corrupted, both methods perform well, but this is not usually the case. Signal attenuations or multipath environments cause non-line-of-sight (NLOS) errors, which corrupt signal propagation and worsen the starting data for algorithms [6], [7], [8], [9], [10].…”

Section: Introductionmentioning

confidence: 99%

“…Many techniques have been applied to overcome NLOS errors, such as Kalman filtering to correct NLDS measurements [7], bootstrapping [11], neural networks [6], [12], or estimating factors to scale NLOS corrupted measurements [9]. If prior statistical knowledge is available, some methods try to distinguish between LOS and NLOS measurements [8] and correct them with expected NLOS errors [13].…”

Section: Introductionmentioning

confidence: 99%

Least Median of Squares for non-line-of-sight error mitigation in GSM localization

Marco

Casas

Asensio

et al. 2008

2008 IEEE 19th International Symposium on Personal, Indoor and Mobile Radio Communications

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Nowadays, almost everyone has a mobile telephone. This requires GSM localizatlon systems to have greater applicability all of the time. The "killer issue" in this field is the well-known non-line-of-sight (NLOS) error, which hinders lecalizatien robustness and accuracy in real scenarios. Many techniques have been developed to deal with this problem, but they usually require prior statistical information or error modeling, or are computationally expensive. In this paper, we present a simple method based on the Least Median of Squares technique to mitigate the NLOS effect. The method we propose is widely used in artificial vision applications and manages to overcome NLOS error effects, yielding higher location accuracy and robustness than other techniques. It can successfully deal with more than half of the available corrupted measurements--no matter how severely-without any previous statistical knowledge--eorrupted measurements are not identifiable--and with reduced computation load.

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“…Nevertheless, they all suffer 2 EURASIP Journal on Applied Signal Processing from non-line-of-sight (NLOS) errors. In the bibliography, a non-line-of-sight error is defined as a large and always positive error that arises when a distance is estimated from a measurement [3,[7][8][9][10][11][12]. It usually occurs when a signal does not follow a straight path from the emitter to the receiver.…”

Section: Introductionmentioning

confidence: 99%

Robust Estimator for Non-Line-of-Sight Error Mitigation in Indoor Localization

Casas

Marco

Guerrero

et al. 2006

EURASIP J. Adv. Signal Process.

124

107

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Indoor localization systems are undoubtedly of interest in many application fields. Like outdoor systems, they suffer from nonline-of-sight (NLOS) errors which hinder their robustness and accuracy. Though many ad hoc techniques have been developed to deal with this problem, unfortunately most of them are not applicable indoors due to the high variability of the environment (movement of furniture and of people, etc.). In this paper, we describe the use of robust regression techniques to detect and reject NLOS measures in a location estimation using multilateration. We show how the least-median-of-squares technique can be used to overcome the effects of NLOS errors, even in environments with little infrastructure, and validate its suitability by comparing it to other methods described in the bibliography. We obtained remarkable results when using it in a real indoor positioning system that works with Bluetooth and ultrasound (BLUPS), even when nearly half the measures suffered from NLOS or other coarse errors.

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Neural networks applications for the prediction of propagation path loss in urban environments

Cited by 23 publications

References 8 publications

A Survey of Localization in Wireless Sensor Network

A Survey of Localization in Wireless Sensor Network

Least Median of Squares for non-line-of-sight error mitigation in GSM localization

Robust Estimator for Non-Line-of-Sight Error Mitigation in Indoor Localization

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