nils-herrmann/clean_scirepeval huggingface dataset:
Echo Cancellation—A Likelihood Ratio Test for Double-Talk Versus Channel Change

Echo cancellers (ECs) are in wide use in both electrical (four-wire to two-wire mismatch) and acoustic (speaker-microphone coupling) applications. One of the main design problems is the control logic for adaptation. Basically, the algorithm weights should be frozen in the presence of double-talk and adapt quickly in the absence of double-talk. The control logic can be quite complicated since it is often not easy to discriminate between the echo signal and the near-end speaker. This paper derives a log-likelihood ratio test (LRT) for deciding between double-talk (freeze weights) and a channel change (adapt quickly) using a stationary Gaussian stochastic input signal model. The probability density function (pdf) of a sufficient statistic under each hypothesis is obtained, and the performance of the test is evaluated as a function of the system parameters. The receiver operating characteristics (ROCs) indicate that it is difficult to correctly decide between double-talk and a channel change based upon a single look. However, postdetection integration of approximately 100 sufficient statistic samples yields a detection probability close to unity (0.99) with a small false-alarm probability (0.01)
Unsupervised Nonlinear Spectral Unmixing Based on a Multilinear Mixing Model

In the community of remote sensing, nonlinear mixture models have recently received particular attention in hyperspectral image processing. In this paper, we present a novel nonlinear spectral unmixing method following the recent multilinear mixing model of Heylen and Scheunders, which includes an infinite number of terms related to interactions between different endmembers. The proposed unmixing method is unsupervised in the sense that the endmembers are estimated jointly with the abundances and other parameters of interest, i.e., the transition probability of undergoing further interactions. Nonnegativity and sum-to-one constraints are imposed on abundances while only nonnegativity is considered for endmembers. The resulting unmixing problem is formulated as a constrained nonlinear optimization problem, which is solved by a block coordinate descent strategy, consisting of updating the endmembers, abundances, and transition probability iteratively. The proposed method is evaluated and compared with existing linear and nonlinear unmixing methods for both synthetic and real hyperspectral data sets acquired by the airborne visible/infrared imaging spectrometer sensor. The advantage of using nonlinear unmixing as opposed to linear unmixing is clearly shown in these examples.
Toward a Sparse Bayesian Markov Random Field Approach to Hyperspectral Unmixing and Classification

Recent work has shown that existing powerful Bayesian hyperspectral unmixing algorithms can be significantly improved by incorporating the inherent local spatial correlations between pixel class labels via the use of Markov random fields. We here propose a new Bayesian approach to joint hyperspectral unmixing and image classification such that the previous assumption of stochastic abundance vectors is relaxed to a formulation whereby a common abundance vector is assumed for pixels in each class. This allows us to avoid stochastic reparameterizations and, instead, we propose a symmetric Dirichlet distributionmodel with adjustable parameters for the common abundance vector of each class. Inference over the proposed model is achieved via a hybrid Gibbs sampler, and in particular, simulated annealing is introduced for the label estimation in order to avoid the local-trap problem. Experiments on a synthetic image and a popular, publicly available real data set indicate the proposed model is faster than and outperforms the existing approach quantitatively and qualitatively. Moreover, for appropriate choices of the Dirichlet parameter, it is shown that the proposed approach has the capability to induce sparsity in the inferred abundance vectors. It is demonstrated that this offers increased robustness in cases where the preprocessing endmember extraction algorithms overestimate the number of active endmembers present in a given scene.
Performance of the maximum likelihood estimators for the parameters of multivariate generalized Gaussian distributions

This paper studies the performance of the maximum likelihood estimators (MLE) for the parameters of multivariate generalized Gaussian distributions. When the shape parameter belongs to ]0, 1[, we have proved that the scatter matrix MLE exists and is unique up to a scalar factor. After providing some elements about this proof, an estimation algorithm based on a Newton-Raphson recursion is investigated. Some experiments illustrate the convergence speed of this algorithm. The bias and consistency of the scatter matrix estimator are then studied for different values of the shape parameter. The performance of the shape parameter estimator is finally addressed by comparing its variance to the Cramér-Rao bound.
A New Multivariate Statistical Model for Change Detection in Images Acquired by Homogeneous and Heterogeneous Sensors

Remote sensing images are commonly used to monitor the earth surface evolution. This surveillance can be conducted by detecting changes between images acquired at different times and possibly by different kinds of sensors. A representative case is when an optical image of a given area is available and a new image is acquired in an emergency situation (resulting from a natural disaster for instance) by a radar satellite. In such a case, images with heterogeneous properties have to be compared for change detection. This paper proposes a new approach for similarity measurement between images acquired by heterogeneous sensors. The approach exploits the considered sensor physical properties and specially the associated measurement noise models and local joint distributions. These properties are inferred through manifold learning. The resulting similarity measure has been successfully applied to detect changes between many kinds of images, including pairs of optical images and pairs of optical-radar images.
Bayesian estimation for the multifractality parameter

Multifractal analysis has matured into a widely used signal and image processing tool. Due to the statistical nature of multifractal processes (strongly non-Gaussian and intricate dependence) the accurate estimation of multifractal parameters is very challenging in situations where the sample size is small (notably including a range of biomedical applications) and currently available estimators need to be improved. To overcome such limitations, the present contribution proposes a Bayesian estimation procedure for the multifractality (or intermittence) parameter. Its originality is threefold: First, the use of wavelet leaders, a recently introduced multiresolution quantity that has been shown to yield significant benefits for multifractal analysis; Second, the construction of a simple yet generic semi-parametric model for the marginals and covariance structure of wavelet leaders for the large class of multiplicative cascade based multifractal processes; Third, the construction of original Bayesian estimators associated with the model and the constraints imposed by multifractal theory. Performance are numerically assessed and illustrated for synthetic multifractal processes for a range of multifractal parameter values. The proposed procedure yields significantly improved estimation performance for small sample sizes.
Changepoint detection using reversible jump MCMC methods

This paper addresses the problem of SAR image segmentation by using reversible jump MCMC sampling. The SAR image segmentation problem is formulated as a Bayesian estimation problem. The reversible jump MCMC algorithm is then used to generate samples distributed according to the joint posterior distribution of the unknown parameters. These samples allow to compute marginal maximum a posteriori estimates for the interesting features. The performance of the proposed methodology is illustrated via several simulation results.
Bayesian change detection for multi-temporal SAR images

This paper addresses the problem of estimating abrupt changes in synthetic aperture radar (SAR) images. The problem is formulated as a Bayesian estimation problem. Appropriate priors allow to take into account the correlations between images recorded at different dates. Unfortunately, the posterior distribution of the unknown parameters cannot be expressed in closed-form. The proposed Bayesian implementation consists of generating samples distributed according this posterior by using MCMC methods. These samples are then used to estimate various interesting features including the posterior changepoint probabilities and the posterior changepoint number. Simulations on synthetic data illustrate the proposed methodology.
Maximum marginal likelihood estimation of the granularity coefficient of a Potts-Markov random field within an MCMC algorithm

This paper addresses the problem of estimating the Potts-Markov random field parameter β jointly with the unknown parameters of a Bayesian image segmentation model. We propose a new adaptive Markov chain Monte Carlo (MCMC) algorithm for performing joint maximum marginal likelihood estimation of β and maximum-a-posteriori unsupervised image segmentation. The method is based on a stochastic gradient adaptation technique whose computational complexity is significantly lower than that of the competing MCMC approaches. This adaptation technique can be easily integrated to existing MCMC methods where β was previously assumed to be known. Experimental results on synthetic data and on a real 3D real image show that the proposed method produces segmentation results that are as good as those obtained with state-of-the-art MCMC methods and at much lower computational cost.
Bayesian sparse estimation of migrating targets for wideband radar

Wideband radar systems are highly resolved in range, which is a desirable feature for mitigating clutter. However, due to a smaller range resolution cell, moving targets are prone to migrate along the range during the coherent processing interval (CPI). This range walk, if ignored, can lead to huge performance degradation in detection. Even if compensated, conventional processing may lead to high sidelobes preventing from a proper detection in case of a multitarget scenario. Turning to a compressed sensing framework, we present a Bayesian algorithm that gives a sparse representation of migrating targets in case of a wideband waveform. Particularly, it is shown that the target signature is the sub-Nyquist version of a virtually well-sampled two-dimensional (2D)-cisoid. A sparse-promoting prior allows then this cisoid to be reconstructed and represented by a single peak without sidelobes. Performance of the proposed algorithm is finally assessed by numerical simulations on synthetic and semiexperimental data. Results obtained are very encouraging and show that a nonambiguous detection mode may be obtained with a single pulse repetition frequency (PRF).
Bivariate pearson distributions for remote sensing images

This paper studies the potential interest of the multivariate Pearson system for the statistical modeling of images acquired by different sensors. These images include optical and synthetic aperture radar (SAR) remote sensing images. The univariate Pearson system has shown good capacities to capture the statistical properties of SAR images. This paper introduces a generalization of this system to multidimensional vectors with a particular attention to the bivariate case. The method of moments is investigated to estimate the unknown parameters of bivariate Pearson distributions. The estimation performance is evaluated using synthetic and real data.
Fuse: A fast multi-band image fusion algorithm

This paper studies a fast multi-band image fusion algorithm for high-spatial low-spectral resolution and low-spatial high-spectral resolution images. The popular forward model and the conventional Gaussian prior are used to form the posterior of the target image. Maximizing the posterior leads to solving a matrix Sylvester equation. By exploiting the properties of the circulant and decimation matrices associated with the fusion problem, a closed-form solution for the corresponding Sylvester equation is obtained, avoiding any iterative update step. Simulation results show that the proposed approach using this closed-form solution achieves the same performance as existing algorithms with the advantage of significantly decreasing the computational complexity of these algorithms.
Subband decomposition using multichannel AR spectral estimation

Subband decomposition has been shown to be a useful tool for spectral estimation, in particular when parametric methods have to be considered. Indeed, the loss of observed samples due to decimation can be compensated by the use of a suitable model, if available. This paper studies a subband multichannel autoregressive spectral estimation (SMASE) method. The proposed method decomposes the observed signal through an appropriate filter bank and processes the decimated signals by means of a multichannel autoregressive (AR) model. This model takes advantage of known correlations between different subband signals. This a priori knowledge allows to improve spectral estimation performance. Simulation results illustrate the interest of the proposed methodology for signals with continuous spectra and for sinusoids.
Bayesian Estimation of Smooth Altimetric Parameters: Application to Conventional and Delay/Doppler Altimetry

This paper proposes a new Bayesian strategy for the smooth estimation of altimetric parameters. The altimetric signal is assumed to be corrupted by a thermal and speckle noise distributed according to an independent and non-identically Gaussian distribution. We introduce a prior enforcing a smooth temporal evolution of the altimetric parameters which improves their physical interpretation. The posterior distribution of the resulting model is optimized using a gradient descent algorithm which allows us to compute the maximum a posteriori estimator of the unknown model parameters. This algorithm has a low computational cost that is suitable for real-time applications. The proposed Bayesian strategy and the corresponding estimation algorithm are evaluated using both synthetic and real data associated with conventional and delay/Doppler altimetry. The analysis of real Jason-2 and CryoSat-2 waveforms shows an improvement in parameter estimation when compared to state-of-the-art estimation algorithms.
Residual component analysis of hyperspectral images for joint nonlinear unmixing and nonlinearity detection

This paper presents a nonlinear mixing model for joint hyperspectral image unmixing and nonlinearity detection. The proposed model assumes that the pixel reflectances are linear mixtures of endmembers, corrupted by an additional nonlinear term and an additive Gaussian noise. A Markov random field is considered for nonlinearity detection based on the spatial structure of the nonlinear terms. The observed image is segmented into regions where nonlinear terms, if present, share similar statistical properties. A Bayesian algorithm is proposed to estimate the parameters involved in the model yielding a joint nonlinear unmixing and nonlinearity detection algorithm. Simulations conducted with synthetic and real data show the accuracy of the proposed unmixing and nonlinearity detection strategy for the analysis of hyperspectral images.
Detection and estimation of multiplicative jumps

Multiplicative jumps have been considered in many applications. These applications include speckle signal in radar images, mechanical vibrations, non-linear time series and random communication models. The problem addressed here is the detection of multiplicative jumps using the Neyman-Pearson test. This test constitutes a reference to which suboptimal detectors can be compared. In practical applications, the parameters of the noise and of the jump have to be estimated. The maximum likelihood estimator and the Cramer Rao bound for these parameters are then studied.
Introduction to the Issue on Advanced Signal Processing for GNSS and Robust Navigation

WE are pleased to have this opportunity to organize a special issue on advances in signal processing for Global Navigation Satellite Systems (GNSS) and robust navigation. This we consider as the first and a seminal issue on this important subject to appear in any IEEE journal or transaction. The goal of this interdisciplinary special issue is to bring together a diverse and complementary set of contributions in the area of signal processing for positioning and navigation, introduce the navigation problems to the larger signal processing community, promote further advances in the area, and help to establish a larger research community around this field.
Bayesian Fusion of Multi-Band Images

This paper presents a Bayesian fusion technique for remotely sensed multi-band images. The observed images are related to the high spectral and high spatial resolution image to be recovered through physical degradations, e.g., spatial and spectral blurring and/or subsampling defined by the sensor characteristics. The fusion problem is formulated within a Bayesian estimation framework. An appropriate prior distribution exploiting geometrical considerations is introduced. To compute the Bayesian estimator of the scene of interest from its posterior distribution, a Markov chain Monte Carlo algorithm is designed to generate samples asymptotically distributed according to the target distribution. To efficiently sample from this high-dimension distribution, a Hamiltonian Monte Carlo step is introduced within a Gibbs sampling strategy. The efficiency of the proposed fusion method is evaluated with respect to several state-of-the-art fusion techniques.
A Bayesian approach for the joint estimation of the multifractality parameter and integral scale based on the Whittle approximation

Multifractal analysis is a powerful tool used in signal processing. Multifractal models are essentially characterized by two parameters, the multifractality parameter c2 and the integral scale A (the time scale beyond which multifractal properties vanish). Yet, most applications concentrate on estimating c2 while the estimation of A is in general overlooked, despite the fact that A potentially conveys important information. Joint estimation of c2 and A is challenging due to the statistical nature of multifractal processes (i.e. the strong dependence and non-Gaussian nature), and has barely been considered. The present contribution addresses these limitations and proposes a Bayesian procedure for the joint estimation of (c2, A). Its originality resides, first, in the construction of a generic multivariate model for the statistics of wavelet leaders for multifractal multiplicative cascade processes, and second, in the use of a suitable Whittle approximation for the likelihood associated with the model. The resulting model enables Bayesian estimators for (c2, A) to also be computed for large sample size. Performance is assessed numerically for synthetic multifractal processes and illustrated for wind-tunnel turbulence data. The proposed procedure significantly improves estimation of c2 and yields, for the first time, reliable estimates for A.
Multipath Mitigation for GNSS Positioning in an Urban Environment Using Sparse Estimation

Multipath (MP) remains the main source of error when using global navigation satellite systems (GNSS) in a constrained environment, leading to biased measurements and thus to inaccurate estimated positions. This paper formulates the GNSS navigation problem as the resolution of an overdetermined system whose unknowns are the receiver position and speed, clock bias and clock drift, and the potential biases affecting GNSS measurements. We assume that only a part of the satellites are affected by MP, i.e., that the unknown bias vector has several zero components, which allows sparse estimation theory to be exploited. The natural way of enforcing this sparsity is to introduce an <inline-formula> <tex-math notation="LaTeX">$\ell _{1}$ </tex-math></inline-formula> regularization associated with the bias vector. This leads to a least absolute shrinkage and selection operator problem that is solved using a reweighted-<inline-formula> <tex-math notation="LaTeX">$\ell _{1}$ </tex-math></inline-formula> algorithm. The weighting matrix of this algorithm is designed carefully as functions of the satellite carrier-to-noise density ratio (<inline-formula> <tex-math notation="LaTeX">$C/N_{0}$ </tex-math></inline-formula>) and the satellite elevations. Experimental validation conducted with real GPS data show the effectiveness of the proposed method as long as the sparsity assumption is respected.
Bayesian sparse estimation of migrating targets in autoregressive noise for wideband radar

In recent work we showed the interest of using sparse representation techniques to estimate a target scene observed by wideband radar systems. However the principle was demonstrated in a white noise background only. In this paper, we present an extended version of our sparse estimation technique that attempts to take into account the (possible) presence of diffuse clutter. More specifically, an autoregressive model is considered for the noise vector. Performance of the technique is studied on synthetic and experimental data. Pertinence of the noise model is discussed.
Single image super-resolution of medical ultrasound images using a fast algorithm

This paper addresses the problem of super-resolution (SR) for medical ultrasound (US) images. Contrary to device-based approaches, we investigate a post-processing method to invert the direct linear model of US image formation. Given the ill-posedness of single image SR, we proposed an ℓp-norm (1 ≤ p ≤ 2) regularizer for the US tissue reflectivity function/image to be estimated. To solve the associated optimization problem, we propose a novel way to explore the decimation and blurring operators simultaneously. As a consequence, we are able to compute the analytical solution for the ℓ2-norm regularized SR problem and to embed the analytical solution to an alternating direction method of multipliers for the ℓp-norm regularized SR problem. The behavior of the proposed algorithm is illustrated using synthetic, simulated and in vivo US data.
Joint Segmentation of Multivariate Astronomical Time Series: Bayesian Sampling With a Hierarchical Model

Astronomy and other sciences often face the problem of detecting and characterizing structure in two or more related time series. This paper approaches such problems using Bayesian priors to represent relationships between signals with various degrees of certainty, and not just rigid constraints. The segmentation is conducted by using a hierarchical Bayesian approach to a piecewise constant Poisson rate model. A Gibbs sampling strategy allows joint estimation of the unknown parameters and hyperparameters. Results obtained with synthetic and real photon counting data illustrate the performance of the proposed algorithm
Nonlinear unmixing of hyperspectral images using a generalized bilinear model

This paper studies a generalized bilinear model and a hierarchical Bayesian algorithm for unmixing hyperspectral images. The proposed model is a generalization of the accepted linear mixing model but also of a bilinear model recently introduced in the literature. Appropriate priors are chosen for its parameters in particular to satisfy the positivity and sum-to-one constraints for the abundances. The joint posterior distribution of the unknown parameter vector is then derived. A Metropolis-within-Gibbs algorithm is proposed which allows samples distributed according to the posterior of interest to be generated and to estimate the unknown model parameters. The performance of the resulting unmixing strategy is evaluated via simulations conducted on synthetic and real data.