In graph signal processing (GSP), prior information on the dependencies in the signal is collected in a graph which is then used when processing or analyzing the signal. Blind source separation (BSS) ...techniques have been developed and analyzed in different domains, but for graph signals the research on BSS is still in its infancy. In this paper, this gap is filled with two contributions. First, a nonparametric BSS method, which is relevant to the GSP framework, is refined, the Cramér-Rao bound (CRB) for mixing and unmixing matrix estimators in the case of Gaussian moving average graph signals is derived, and for studying the achievability of the CRB, a new parametric method for BSS of Gaussian moving average graph signals is introduced. Second, we also consider BSS of non-Gaussian graph signals and two methods are proposed. Identifiability conditions show that utilizing both graph structure and non-Gaussianity provides a more robust approach than methods which are based on only either graph dependencies or non-Gaussianity. It is also demonstrated by numerical study that the proposed methods are more efficient in separating non-Gaussian graph signals.
Abstract
The surroundings of massive protostars constitute an accretion disc which has numerically been shown to be subject to fragmentation and responsible for luminous accretion-driven outbursts. ...Moreover, it is suspected to produce close binary companions which will later strongly influence the star's future evolution in the Hertzsprung–Russel diagram. We present three-dimensional gravitation-radiation-hydrodynamic numerical simulations of 100 M⊙ pre-stellar cores. We find that accretion discs of young massive stars violently fragment without preventing the (highly variable) accretion of gaseous clumps on to the protostars. While acquiring the characteristics of a nascent low-mass companion, some disc fragments migrate on to the central massive protostar with dynamical properties showing that its final Keplerian orbit is close enough to constitute a close massive protobinary system, having a young high- and a low-mass components. We conclude on the viability of the disc fragmentation channel for the formation of such short-period binaries, and that both processes – close massive binary formation and accretion bursts – may happen at the same time. FU-Orionis-type bursts, such as observed in the young high-mass star S255IR−NIRS3, may not only indicate ongoing disc fragmentation, but also be considered as a tracer for the formation of close massive binaries – progenitors of the subsequent massive spectroscopic binaries – once the high-mass component of the system will enter the main-sequence phase of its evolution. Finally, we investigate the Atacama Large (sub-)Millimeter Array observability of the disc fragments.
We address the problem of maximum likelihood (ML) direction-of-arrival (DOA) estimation in unknown spatially correlated noise fields using sparse sensor arrays composed of multiple widely separated ...subarrays. In such arrays, intersubarray spacings are substantially larger than the signal wavelength, and therefore, sensor noises can be assumed to be uncorrelated between different subarrays. This leads to a block-diagonal structure of the noise covariance matrix which enables a substantial reduction of the number of nuisance noise parameters and ensures the identifiability of the underlying DOA estimation problem. A new deterministic ML DOA estimator is derived for this class of sparse sensor arrays. The proposed approach concentrates the ML estimation problem with respect to all nuisance parameters. In contrast to the analytic concentration used in conventional ML techniques, the implementation of the proposed estimator is based on an iterative procedure, which includes a stepwise concentration of the log-likelihood (LL) function. The proposed algorithm is shown to have a straightforward extension to the case of uncalibrated arrays with unknown sensor gains and phases. It is free of any further structural constraints or parametric model restrictions that are usually imposed on the noise covariance matrix and received signals in most existing ML-based approaches to DOA estimation in spatially correlated noise.
The uniform white noise assumption is one of the basic assumptions in most of the existing direction-of-arrival (DOA) estimation methods. In many applications, however, the nonuniform white noise ...model is more adequate. Then, the noise variances at different sensors have to be also estimated as nuisance parameters while estimating DOAs. In this letter, different from the existing iterative methods that address the problem of nonuniform noise, a non-iterative two-phase subspace-based DOA estimation method is proposed. The first phase of the method is based on estimating the noise subspace via eigendecomposition (ED) of some properly designed matrix and it avoids estimating the noise covariance matrix. In the second phase, the results achieved in the first phase are used to estimate the noise covariance matrix, followed by estimating the noise subspace via generalized ED. Since the proposed method estimates DOAs in a non-iterative manner, it is computationally more efficient and has no convergence issues as compared to the existing methods. Simulation results demonstrate better performance of the proposed method as compared to other existing state-of-the-art methods.
•A robust adaptive beamforming problem for general-rank signal model is considered.•The worst-case SINR maximization formulation is established.•The closed-form optimal value of the minimization ...problem of the least-squares residual over the matrix errors with an induced norm constraint is derived.•With the closed-form result, the worst-case SINR maximization problem is approximated by a sequence of SOCPs.
The robust adaptive beamforming (RAB) problem for general-rank signal model with an uncertainty set defined through a matrix induced norm is considered. The worst-case signal-to-interference-plus-noise ratio (SINR) maximization RAB problem is formulated. First, the closed-form optimal value for a minimization problem of the least-squares residual over the matrix errors with an induced lp,q-norm constraint is derived. Then, the maximization problem is reformulated into the maximization of the difference between an l2-norm function and an lq-norm function, with a convex quadratic constraint. It is shown that for any q≥1 in the set of rational numbers, the maximization problem can be approximated by a sequence of second-order cone programming problems, with the ascent optimal values. The resultant beamvector for some q in the set with the maximal actual array output SINR, is treated as the candidate making the RAB design improved the most. In addition, a generalized RAB problem of maximizing the difference between an lp-norm function and an lq-norm function with the convex quadratic constraint is studied, and the actual array output SINR is further enhanced by properly selecting p and q. Simulation examples are presented to demonstrate the improved performance of the robust beamformers for certain matrix induced lp,q-norms.
Ultrasound imaging (UI) is characterized by the presence of multiplicative speckle noise and various acquisition artefacts. Designing ultrasound (US) similarity measures thus requires a particular ...attention. In the specific context of motion estimation, incorporating US characteristics does not only benefit traditional methods but also learning-based approaches, which are highly sensitive to the quality of training data. Deriving similarity measures from a maximum likelihood (ML) perspective allows us to take these specificities into account. As opposed to the classical Rayleigh modelling, the proposed similarity measures incorporate more realistic scattering conditions, such as, varying speckle densities and shadowing. Specifically, the deviations from the Rayleigh statistics are modelled using the <inline-formula><tex-math notation="LaTeX">t</tex-math></inline-formula>-distribution for the complex radio-frequency (RF) signals and the Nakagami-Gamma (NG) compound model for the echo amplitudes. Furthermore, the model parameters are learnt patch-wise, which leads to data-adaptive similarity measures. The proposed criteria are investigated in the context of motion estimation using synthetic, phantom, as well as 2D and 3D in vivo images. The experimental results show an improvement in performance and robustness in comparison to the classical Rayleigh-based approach and state-of-the-art similarity measures.
We present new Submillimeter Array (SMA) observations of CO(2-1) outflows toward young, embedded protostars in the Perseus molecular cloud as part of the Mass Assembly of Stellar Systems and their ...Evolution with the SMA (MASSES) survey. For 57 Perseus protostars, we characterize the orientation of the outflow angles and compare them with the orientation of the local filaments as derived from Herschel observations. We find that the relative angles between outflows and filaments are inconsistent with purely parallel or purely perpendicular distributions. Instead, the observed distribution of outflow-filament angles are more consistent with either randomly aligned angles or a mix of projected parallel and perpendicular angles. A mix of parallel and perpendicular angles requires perpendicular alignment to be more common by a factor of ∼3. Our results show that the observed distributions probably hold regardless of the protostar's multiplicity, age, or the host core's opacity. These observations indicate that the angular momentum axis of a protostar may be independent of the large-scale structure. We discuss the significance of independent protostellar rotation axes in the general picture of filament-based star formation.
Robust iterative fitting of multilinear models Vorobyov, S.A.; Yue Rong; Sidiropoulos, N.D. ...
IEEE transactions on signal processing,
08/2005, Letnik:
53, Številka:
8
Journal Article
Recenzirano
Odprti dostop
Parallel factor (PARAFAC) analysis is an extension of low-rank matrix decomposition to higher way arrays, also referred to as tensors. It decomposes a given array in a sum of multilinear terms, ...analogous to the familiar bilinear vector outer products that appear in matrix decomposition. PARAFAC analysis generalizes and unifies common array processing models, like joint diagonalization and ESPRIT; it has found numerous applications from blind multiuser detection and multidimensional harmonic retrieval, to clustering and nuclear magnetic resonance. The prevailing fitting algorithm in all these applications is based on (alternating) least squares, which is optimal for Gaussian noise. In many cases, however, measurement errors are far from being Gaussian. In this paper, we develop two iterative algorithms for the least absolute error fitting of general multilinear models. The first is based on efficient interior point methods for linear programming, employed in an alternating fashion. The second is based on a weighted median filtering iteration, which is particularly appealing from a simplicity viewpoint. Both are guaranteed to converge in terms of absolute error. Performance is illustrated by means of simulations, and compared to the pertinent Crame/spl acute/r-Rao bounds (CRBs).
The SWM4-DP polarizable water model G. Lamoureux, A.D. MacKerell, Jr., B. Roux, J. Chem. Phys. 119 (2003) 5185, based on classical Drude oscillators, is re-optimized for negatively charged Drude ...particles. The new model, called SWM4-NDP, will be incorporated into a polarizable biomolecular force field currently in development. It is calibrated to reproduce important properties of the neat liquid at room temperature and pressure: vaporization enthalpy, density, static dielectric constant and self-diffusion constant. In this Letter, we also show that it yields the correct liquid shear viscosity and free energy of hydration.
In this paper, we derive an ambiguity function (AF) for the transmit beamspace (TB)-based MIMO radar for the case of far-field targets and narrow-band waveforms. The effects of transmit coherent ...processing gain and waveform diversity are incorporated into the AF definition. To cover all the phase information conveyed by different factors, we introduce the equivalent transmit phase centers. The newly defined AF serves as a generalized AF form for which the phased-array (PA) and traditional MIMO radar AFs are important special cases. We establish relationships among the defined TB-based MIMO radar AF and the existing AF results, including the Woodward's AF, the AFs defined for traditional colocated MIMO radar, and also the PA radar AF, respectively. Moreover, we compare the TB-based MIMO radar AF with the square-summation-form AF definition and identify two limiting cases to bound its "clear region" in Doppler-delay domain that is free of sidelobes. Corresponding bounds for these two cases are derived, and it is shown that the bound for the worst case is inversely proportional to the number of transmitted waveforms K, whereas the bound for the best case is independent of K. The actual "clear region" of the TB-based MIMO radar AF depends on the array configuration and is in between of the worst- and best-case bounds. We propose a new TB design strategy to reduce the levels of the AF sidelobes and show in simulations that proper design of the TB matrix leads to reduction of the relative sidelobe levels of the TB-based MIMO radar AF.
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