A selective model to suppress NLOS signals in angle-of-arrival (AOA) location estimation

Li, Xiong

doi:10.1109/pimrc.1998.733610

Cited by 87 publications

(56 citation statements)

References 4 publications

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“…Niculescu et al [16] simulate AoA-based localization in an ad hoc mesh network. AoA has also been proposed in CDMA mobile cellular systems [34], in particular as a hybrid approach between TDoA and AoA [8,32], and also in concert with interference cancellation and ToA [27].…”

Section: Related Workmentioning

confidence: 99%

Towards fine-grained radio-based indoor location

Xiong

Jamieson

2012

Proceedings of the Twelfth Workshop on Mobile Computing Systems &Amp; Applications - HotMobile '12

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Location systems are key to a rich experience for mobile users. When they roam outdoors, mobiles can usually count on a clear GPS signal for an accurate location, but indoors, GPS usually fades, and so up until recently, mobiles have had to rely mainly on rather coarse-grained signal strength readings for location. What has changed this status quo is the recent trend of dramatically increasing numbers of antennas at the indoor AP, mainly to bolster capacity and coverage with multiple-input, multiple-output (MIMO) techniques. In the near future, the number of antennas at the access point will increase several-fold, to meet increasing demands for wireless capacity with MIMO links, spatial division multiplexing, and interference management. We thus observe an opportunity to revisit the important problem of localization with a fresh perspective. This paper presents the design and experimental evaluation of ArrayTrack, an indoor location system that uses MIMO-based techniques to track wireless clients in real time as they roam about a building. We prototype ArrayTrack on a WARP platform, emulating the capabilities of an inexpensive 802.11 wireless access point. Our results show that ArrayTrack can pinpoint 33 clients spread out over an indoor office environment to within a 36 cm location accuracy.

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Section: Related Workmentioning

confidence: 99%

Towards fine-grained radio-based indoor location

Xiong

Jamieson

2012

Proceedings of the Twelfth Workshop on Mobile Computing Systems &Amp; Applications - HotMobile '12

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“…In matrix notation, the output of the virtual array is given as (13) where is the matrix of the array responses of all sensors for all DOA"s. The correlator cross-correlates the array signal with that of the output of the virtual array as (14) where R is K L matrix. The spatial spectrum of the SDMA receiver is then (15) where (16) The peaks of P SDMA correspond to the DOAs of the incident signals.…”

Section: International Journal Of Computer Applications (0975 -8887) mentioning

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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“…Other hybrid approaches combine TOA, AOA and RSS measurements to identify the nodes in NLOS [8] and mitigate the NLOS error [9]. In [10], a simple outlier detection problem is proposed to identify and discard AOA measurements taken in NLOS conditions based on the fact that these exhibit a completely different statistical behavior. Another option is the use of machine learning algorithms for the identification of outliers in WSNs, as proposed in [11].…”

Section: Introduction Wmentioning

confidence: 99%

Distributed AOA-based source positioning in NLOS with sensor networks

Giménez-Febrer

Pagès-Zamora

Pereira

et al. 2015

2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Abstract-This paper focuses on the problem of positioning a source using angle-of-arrival measurements taken by a wireless sensor network in which some of the nodes experience non lineof-sight (LOS) propagation conditions. In order to mitigate the errors induced by the nodes in NLOS, we derive an algorithm that combines the expectation-maximization algorithm with a weighted least-squares estimation of the source position so that the nodes in NLOS are eventually identified and discarded. Moreover, a distributed version of this algorithm based on a diffusion strategy that iteratively refines the position estimate while driving the network to a consensus is presented.

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A selective model to suppress NLOS signals in angle-of-arrival (AOA) location estimation

Cited by 87 publications

References 4 publications

Towards fine-grained radio-based indoor location

Towards fine-grained radio-based indoor location

Passive Source Localization in a Randomly Distributed Wireless Sensor Networks

Distributed AOA-based source positioning in NLOS with sensor networks

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