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Pau Closas
Northeastern University
GNSS applicationsKalman filterElectronic engineeringComputer scienceParticle filter
162Publications
14H-index
1,051Citations
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Publications 178
Newest
#1Daniel MedinaH-Index: 7
#2Lorenzo OrtegaH-Index: 1
Last. Eric ChaumetteH-Index: 11
view all 6 authors...
1 CitationsSource
#1Parisa Borhani-Darian (NU: Northeastern University)
#2Pau Closas (NU: Northeastern University)H-Index: 14
This paper investigates the use of data-driven models, popular in the machine learning literature, as an alternative to well-engineered signal processing blocks used in state-of-the-art GNSS receivers. Acknowledging that the latter are optimally designed and extensively tested, it is also agreed that when the nominal models do not hold the performance of the receiver might degrade. Particularly, we investigate the use of data-driven models in the signal acquisition stage of the receiver by addre...
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#1Gerald LaMountain (NU: Northeastern University)
#2Pau Closas (NU: Northeastern University)H-Index: 14
GNSS denial via jamming is a low skilled attack which can be performed by nearly anyone using tools which are readily available through the online marketplace. Methods of jammer mitigation such as beamforming or other active methodologies require an estimation of the location of the jamming signal source. There is interest in developing systems which can be used to identify and locate the sources of broadcast signals, either for the purposes of augmenting mitigation or for the purposes of taking...
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#1Tales Imbiriba (NU: Northeastern University)
#2Peng Wu (NU: Northeastern University)
Last. Pau Closas (NU: Northeastern University)H-Index: 14
view all 5 authors...
This paper investigates the use of Gaussian Processes (GP) for RSS fingerprinting-based navigation. In particular, a recursive GP scheme to adapt the fingerprints as they evolve over time is discussed which accounts for the uncertainty of position labels associated to new RSS measurements. The marginalization over the uncertainty of position labels is here implemented numerically through cubature rules, which is seen from computer simulations to enhance field estimation performance and, ultimate...
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This letter investigates on the derivation of good log likelihood ratio (LLR) approximations under uncorrelated fading channels with partial statistical channel state information (CSI) at the receiver. While previous works focused mainly on solutions exploiting full statistical CSI over the normalized Rayleigh fading channel, in this letter, a Bayesian approach based on conjugate prior analysis is proposed to derive LLR values that only uses moments of order one and two associated with the rando...
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#1Jordi Vila-Valls (University of Toulouse)H-Index: 8
#2Damien Vivet (University of Toulouse)H-Index: 4
Last. Pau Closas (NU: Northeastern University)H-Index: 14
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Abstract This article introduces a new class of recursive linearly constrained minimum variance estimators (LCMVEs) that provides additional robustness to modeling errors. To achieve that robustness, a set of non-stationary linear constraints are added to the standard LCMVE that allow for a closed form solution that becomes appealing in sequential implementations of the estimator. Indeed, a key point of such recursive LCMVE is to be fully adaptive in the context of sequential estimation as it al...
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#1Victor ElviraH-Index: 15
#2Luca MartinoH-Index: 18
Last. Pau ClosasH-Index: 14
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Importance sampling (IS) and numerical integration methods are usually employed for approximating moments of complicated targeted distributions. In its basic procedure, the IS methodology randomly draws samples from a proposal distribution and weights them accordingly, accounting for the mismatch between the target and proposal. In this work, we present a general framework of numerical integration techniques inspired by the IS methodology. The framework can also be seen as an incorporation of de...
1 Citations
The recent evolution of hyperspectral imaging technology and the proliferation of new emerging applications presses for the processing of multiple temporal hyperspectral images. In this work, we propose a novel spectral unmixing (SU) strategy using physically motivated parametric endmember representations to account for temporal spectral variability. By representing the multitemporal mixing process using a state-space formulation, we are able to exploit the Bayesian filtering machinery to estima...
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#1Daniel MedinaH-Index: 7
#2Haoqing LiH-Index: 1
Last. Pau ClosasH-Index: 14
view all 4 authors...
Navigation problems are generally solved applying least-squares (LS) adjustments. Techniques based on LS can be shown to perform optimally when the system noise is Gaussian distributed and the parametric model is accurately known. Unfortunately, real world problems usually contain unexpectedly large errors, so-called outliers, that violate the noise model assumption, leading to a spoiled solution estimation. In this work, the framework of robust statistics is explored to provide robust solutions...
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