Robust localization over obstructed interferences for inbuilding wireless applications

Kong, Young-Bae; Kwon, Younggoo; Park, Gwi-Tae

doi:10.1109/tce.2009.4814421

Cited by 31 publications

(28 citation statements)

References 17 publications

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“…They can obtain the desirable results with enough training samples. In [24], a min-max strategy is invented to identify the propagation paths by constructing a detection region according to the range measurements. If the localization is far away from the edge of the region, the propagation is an NLOS propagation.…”

Section: Related Workmentioning

confidence: 99%

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Mean Shift-Based Mobile Localization Method in Mixed LOS/NLOS Environments for Wireless Sensor Network

Wang

et al. 2017

Journal of Sensors

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Mobile localization estimation is a significant research topic in the fields of wireless sensor network (WSN), which is of concern greatly in the past decades. Non-line-of-sight (NLOS) propagation seriously decreases the positioning accuracy if it is not considered when the mobile localization algorithm is designed. NLOS propagation has been a serious challenge. This paper presents a novel mobile localization method in order to overcome the effects of NLOS errors by utilizing the mean shift-based Kalman filter. The binary hypothesis is firstly carried out to detect the measurements which contain the NLOS errors. For NLOS propagation condition, mean shift algorithm is utilized to evaluate the means of the NLOS measurements and the data association method is proposed to mitigate the NLOS errors. Simulation results show that the proposed method can provide higher location accuracy in comparison with some traditional methods.

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Section: Related Workmentioning

confidence: 99%

“…In the LOS environment, E n k is computed according to (14). In the NLOS environment, E n k is computed according to (24). The final state estimate can be obtained using the following equation:…”

Section: Journal Of Sensorsmentioning

confidence: 99%

Mean Shift-Based Mobile Localization Method in Mixed LOS/NLOS Environments for Wireless Sensor Network

Wang

et al. 2017

Journal of Sensors

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“…The robust multilateration method [7] can avoid this problem by minimizing the proposed objective function. The Min-Max algorithm is proposed in [8] to detect and compensate the large attenuation measurement error in obstructed environments. The basic approach is to divide the network area into small grids and perform the localization using the position probability grid.…”

Section: Related Workmentioning

confidence: 99%

An Indoor Localization Strategy for a Mini-UAV in the Presence of Obstacles

Cheng

Zhang

et al. 2012

International Journal of Advanced Robotic Systems

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In this paper, we propose a novel approach to mini-UAV localization in a wireless sensor network. We firstly employ the environment adaptive RSS parameters' estimation method to estimate the parameters of range estimation model. However, the direct path from the target to a beacon is blocked by obstacles in a complicated indoor environment. So the proposed method, which employs a sequential probability ratio test to identify whether the measurement contains non-line of sight (NLOS) errors, is tolerant to parameter fluctuations. Finally, a particle swarm optimization-based method is proposed to solve the established objective function. Simulation results show that the proposed method achieved relatively higher localization accuracy. In addition, the performance analyses, carried out for a realistic indoor environment, shows that the proposed method still preserves the same localization accuracy.

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“…However, since satellite signals do not penetrate buildings adequately, it is not possible for such systems to function in indoor environments, which has led to a variety of proposed methods for indoor localization and tracking in an attempt to provide seamless and ubiquitous services for mobile users [2][3][4][5][6][7][8][9][10][11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Robust indoor localization and tracking using GSM fingerprints

Tian

Denby

Ahriz

et al. 2015

J Wireless Com Network

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The article presents an easy to implement approach for indoor localization and navigation that combines Bayesian filtering with support vector machine classifiers to associate high-dimensionality cellular telephone network received signal strength fingerprints to distinct spatial regions. The technique employs a "space sampling" and a "time sampling" scheme in the training procedure, and the Bayesian filter allows introducing a priori information on room layout and target trajectories, resulting in robust room-level indoor localization.

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Robust localization over obstructed interferences for inbuilding wireless applications

Cited by 31 publications

References 17 publications

Mean Shift-Based Mobile Localization Method in Mixed LOS/NLOS Environments for Wireless Sensor Network

Mean Shift-Based Mobile Localization Method in Mixed LOS/NLOS Environments for Wireless Sensor Network

An Indoor Localization Strategy for a Mini-UAV in the Presence of Obstacles

Robust indoor localization and tracking using GSM fingerprints

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