A non-line-of-sight equalization scheme for wireless cellular location

Khajehnouri, N.; Sayed, Ali H.

doi:10.1109/icassp.2003.1201740

Cited by 29 publications

(12 citation statements)

References 8 publications

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“…The angle error n b is composed of the LOS angle error and NLOS angle error. The LOS angle error can be modeled as zero mean Gaussian random variable with standard deviation of approximately 3°and the NLOS angle error can be considered as a zero mean Gaussian random variable with standard deviation 5°-10° [14].…”

Section: Simulation Resultsmentioning

confidence: 99%

“…The simulation parameters refer to [14], the LOS range error n i is modeled as zero mean Gaussian distribution with the same standard deviation between 30 and 60 m. The NLOS range error e i is the speed of light multiply the propagation delay between direct path and other paths. The probability density function of the propagation delay is modeled as an exponential distribution as follow…”

Section: Simulation Resultsmentioning

confidence: 99%

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Adaptive NLOS Mitigation Location Algorithm in Wireless Cellular Network

Wang

2015

Wireless Pers Commun

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In a cellular network, a location scenario is considered in this paper, which the home base station (BS) measures the range and angle while neighboring BSs measure the ranges. Based on the range and angle measurements, two adaptive non-line-of-sight (NLOS) identification and mitigation location algorithms with low complexity are proposed. The main idea of our proposed algorithms is to search the optimal subset of range and angle measurements by utilizing the hybrid lines of position algorithm and comparing the normalized residual error to perform the NLOS identification and location. Compared with the existing algorithms, the proposed algorithms have three advantages: one is different to the existing algorithms that require at least three BSs, the proposed algorithms can be suitable for two BSs scenario. Second is the low complexity of our proposed algorithms compared with the existing algorithms. The last is simulation results show that the proposed algorithms outperform the existing algorithms in bad urban and urban environments.

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Section: Simulation Resultsmentioning

confidence: 99%

Section: Simulation Resultsmentioning

confidence: 99%

Adaptive NLOS Mitigation Location Algorithm in Wireless Cellular Network

Wang

2015

Wireless Pers Commun

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“…In [10][11][12], weighting approaches are proposed. The authors in [10,11] used geometrical equations to calculate the weights to be applied to the different measurements in order to compensate for the NLoS effects. In [12], a residual fitting is introduced in a scaling factor.…”

Section: Introductionmentioning

confidence: 99%

Mobile Localization Under Non-Line-of-Sight Conditions Using Scattering Information

Zhaounia¹,

Landolsi

Bouallègue³

2010

Int J Wireless Inf Networks

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A novel mobile localization algorithm under non-line-of-sight (NLoS) conditions involving at most two base stations (BS) is presented. By using such a small number of BSs, the proposed technique avoids problems related to weak mobile hearability and excessive network messaging overload. Based on a scattering model, the NLoS hybrid approximate maximum likelihood (NLoS HAML) approach presented herein estimates the distances between the mobile station (MS) and the scatterers through the measured hybrid time-of-arrival/angle-of-arrival (ToA/ AoA) statistics. Then, it matches the calculated distances to a known scatterers' distribution in a maximum likelihood (ML) sense. Numerical results prove that the proposed algorithm outperforms previous methods by providing an accuracy enhancement that reaches 60%. It is also shown that the performance enhancement provided by the proposed algorithm is maximized when the adequate scatterers distribution is adopted.

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“…The produced array outputs are then orthogonal or nearly orthogonal. In matrix notation, the output of the array is (10) where X is the incident signal (11) and is the matrix of the spreading sequences (12). (11) (12)…”

Section: Sdma Algorithmmentioning

confidence: 99%

Passive Source Localization in a Randomly Distributed Wireless Sensor Networks

Niaz¹,

Khan²,

Shafi³

2011

IJCA

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This paper proposes a source localization scheme using random arrays of Wireless Sensor Networks (WSN). A Total Least Square (TLS) estimator is proposed which improves the result of the location of source node. Using a relatively new Direction of Arrival (DOA) estimation technique Space Division Multiple Access (SDMA) receiver the proposed solution is able to perform localization in a multipath environment. The propose scheme considers both Line of Sight (LOS) and Non Line of Sight (NLOS) signals to perform the localization with the TLS estimator which is efficient than a simple Least Square (LS) estimator. Simulation results are included to demonstrate that the proposed solution provides an improved estimate by exploiting the NLOS information, SDMA receiver and using TLS estimator.

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A non-line-of-sight equalization scheme for wireless cellular location

Cited by 29 publications

References 8 publications

Adaptive NLOS Mitigation Location Algorithm in Wireless Cellular Network

Adaptive NLOS Mitigation Location Algorithm in Wireless Cellular Network

Mobile Localization Under Non-Line-of-Sight Conditions Using Scattering Information

Passive Source Localization in a Randomly Distributed Wireless Sensor Networks

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