nils-herrmann/clean_scirepeval huggingface dataset:
Comparison between two non-minimum phase system estimators

The most commonly used moving average (MA) parameter estimators are approximations to the maximum likelihood estimator (MLE). The use of the MLE for parameter estimation can be justified for large data records because of its asymptotic unbiased and minimum variance properties. Moreover, it is well known that the MLE has a zero error asymptotically. A new estimator of the MA parameters was also developed. The main property of this estimator is to give, in absence of noise, a zero error for a finite signal point number with a high known probability. The performance of this estimator in the presence of an additive Gaussian or bounded noise have been studied. It is not restricted to minimum phase systems. The aim of this paper is to compare its performance with those obtained with a higher order estimator (GM estimator) developed by Giannakis and Mendel (1989) for identification of non-minimum phase systems driven by binary inputs.<<ETX>>
Fusion of high resolution optical and SAR images with vector data bases for change detection

This paper addresses the issue of cartographic database creation or update using high resolution SAR and optical images. In cartographic applications, objects of interest are mainly buildings and roads. This paper proposes a processing chain to update building databases. The approach is composed of two steps. First, the presence of each database object is checked in the images. Then, we verify if objects coming from an image segmentation should be added in the database. To do those two steps, features are extracted from images in the neighborhood of the considered object. The object removal/inclusion in the database is based on a score obtained by the fusion of features in the framework of Dempster Shafer evidence theory.
Joint detection of variance changes using hierarchical Bayesian analysis

This paper addresses the problem of detecting variance changes in time-series coming from two different sensors. The two sequences are modeled as zero-mean white Gaussian sequences with piecewise constant variances. Bayesian inference allows to define interesting priors which reflect the correlations between the two change-point sequences. Unfortunately, the Bayesian estimators for the change-point parameters cannot be expressed in closed-form. A Metropolis-within-Gibbs algorithm allows to generate samples distributed according to the posterior distributions of the unknown parameters. The hierarchical structure of the Bayesian model is also used to estimate the unknown hyperparameters.
MRSI data unmixing using spatial and spectral priors in transformed domains

In high-grade gliomas, the tumor boundaries and the degree of infiltration are difficult to define due to their heterogeneous composition and diffuse growth pattern. Magnetic Resonance Spectroscopic Imaging (MRSI) is a non-invasive technique able to provide information on brain tumor biology not available from conventional anatomical imaging. In this paper we propose a blind source separation (BSS) algorithm for brain tissue classification and visualization of tumor spread using MRSI data. The proposed algorithm imposes relaxed non-negativity in the direct domain along with spatial-spectral regularizations in a transformed domain. The optimization problem is efficiently solved in a two-step approach using the concept of proximity operators. Vertex component analysis (VCA) is proposed to estimate the number of sources. Comparisons with state-of-the-art BSS algorithms on in-vivo MRSI data show the efficiency of the proposed algorithm. The presented method provides patterns that can easily be related to a specific tissue (normal, tumor, necrosis, hypoxia, edema or infiltration). Unlike other BSS methods dedicated to MRSI data, it can handle spectra with negative peaks and results are not sensitive to the initialization strategy. In addition, it is robust against noisy or bad-quality spectra.
Knowledge-Aided STAP in Heterogeneous Clutter using a Hierarchical Bayesian Algorithm

The problem of estimating the covariance matrix of a primary vector from heterogeneous samples and some prior knowledge is addressed, under the framework of knowledge-aided space-time adaptive processing (KA-STAP). More precisely, a Gaussian scenario is considered where the covariance matrix of the secondary data may differ from the one of interest. Additionally, some knowledge on the primary data is supposed to be available and summarized in a prior matrix. Two KA-estimation schemes are presented in a Bayesian framework whereby the minimum mean square error (MMSE) estimates are derived. The first scheme is an extension of a previous work and takes into account the nonhomogeneity via an original relation. In search of simplicity and to reduce the computational load, a second estimation scheme, less complex, is proposed and omits the fact that the environment may be heterogeneous. Along the estimation process, not only the covariance matrix is estimated but also some parameters representing the degree of a priori and/or the degree of heterogeneity. Performance of the two approaches are then compared using STAP synthetic data. STAP filter shapes are analyzed and also compared with a colored loading technique.
A partially collapsed Gibbs sampler with accelerated convergence for EEG source localization

This paper addresses the problem of designing efficient sampling moves in order to accelerate the convergence of MCMC methods. The Partially collapsed Gibbs sampler (PCGS) takes advantage of variable reordering, marginalization and trimming to accelerate the convergence of the traditional Gibbs sampler. This work studies two specific moves which allow the convergence of the PCGS to be further improved. It considers a Bayesian model where structured sparsity is enforced using a multivariate Bernoulli Laplacian prior. The posterior distribution associated with this model depends on mixed discrete and continuous random vectors. Due to the discrete part of the posterior, the conventional PCGS gets easily stuck around local maxima. Two Metropolis-Hastings moves based on multiple dipole random shifts and inter-chain proposals are proposed to overcome this problem. The resulting PCGS is applied to EEG source localization. Experiments conducted with synthetic data illustrate the effectiveness of this PCGS with accelerated convergence.
Including Antenna Mispointing in a Semi-Analytical Model for Delay/Doppler Altimetry

Delay/Doppler altimetry (DDA) aims at reducing the measurement noise and increasing the along-track resolution in comparison with conventional pulse-limited altimetry. In a previous paper, we have proposed a semi-analytical model for DDA, which considers some simplifications as the absence of mispointing antenna. This paper first proposes a new analytical expression for the flat surface impulse response (FSIR), considering antenna mispointing angles, a circular antenna pattern, no vertical speed effect, and uniform scattering. The 2-D delay/Doppler map is then obtained by a numerical computation of the convolution between the proposed analytical function, the probability density function of the heights of the specular scatterers, and the time/frequency point target response of the radar. The approximations used to obtain the semi-analytical model are analyzed, and the associated errors are quantified by analytical bounds for these errors. The second contribution of this paper concerns the estimation of the parameters associated with the multilook semi-analytical model. Two estimation strategies based on the least squares procedure are proposed. The proposed model and algorithms are validated on both synthetic and real waveforms. The obtained results are very promising and show the accuracy of this generalized model with respect to the previous model assuming zero antenna mispointing.
Knowledge-Aided Bayesian Detection in Heterogeneous Environments

We address the problem of detecting a signal of interest in the presence of noise with unknown covariance matrix, using a set of training samples. We consider a situation where the environment is not homogeneous, i.e., when the covariance matrices of the primary and the secondary data are different. A knowledge-aided Bayesian framework is proposed, where these covariance matrices are considered as random, and some information about the covariance matrix of the training samples is available. Within this framework, the maximum a priori (MAP) estimate of the primary data covariance matrix is derived. It is shown that it amounts to colored loading of the sample covariance matrix of the secondary data. The MAP estimate is in turn used to yield a Bayesian version of the adaptive matched filter. Numerical simulations illustrate the performance of this detector, and compare it with the conventional adaptive matched filter
Maximum-likelihood estimation of the polarization degree from two multi-look intensity images

Information contained in polarimetric images can be characterized by a scalar parameter called the polarization degree. This parameter is usually estimated using four polarimetric images. However, acquisition and registration of four images are complex and costly. Thus, reducing the number of required images can be of great interest for practical applications. In coherent illumination, these images are degraded by speckle noise. This noise can be reduced by transforming the original single-look images into multi-look images. We provide maximum likelihood estimators of the polarization degree in the general case of multi-look intensity images. The estimators are derived based on only two measurements, under coherent illumination and fully developed speckle. We evaluate our method on synthetic data and compare its performance with that of moment-based methods.
Unsupervised Unmixing of Hyperspectral Images Accounting for Endmember Variability

This paper presents an unsupervised Bayesian algorithm for hyperspectral image unmixing, accounting for endmember variability. The pixels are modeled by a linear combination of endmembers weighted by their corresponding abundances. However, the endmembers are assumed random to consider their variability in the image. An additive noise is also considered in the proposed model, generalizing the normal compositional model. The proposed algorithm exploits the whole image to benefit from both spectral and spatial information. It estimates both the mean and the covariance matrix of each endmember in the image. This allows the behavior of each material to be analyzed and its variability to be quantified in the scene. A spatial segmentation is also obtained based on the estimated abundances. In order to estimate the parameters associated with the proposed Bayesian model, we propose to use a Hamiltonian Monte Carlo algorithm. The performance of the resulting unmixing strategy is evaluated through simulations conducted on both synthetic and real data.
A kernel density-based particle filter for state and time-varying parameter estimation in nonlinear state-space models

In linear/nonlinear dynamical systems, there are many situations where model parameters cannot be obtained a priori or vary with time. As a consequence, the estimation algorithms that are based on the exact knowledge of these model parameters cannot be accurate in this context. In this work, a kernel density-based particle filter is investigated to jointly estimate the states and unknown time-varying parameters of a dynamical system described by nonlinear state and measurement equations. The approach combines an auxiliary particle filter with the kernel smoothing method so as to obtain a stationary kernel density for the unknown parameters. The performance of the proposed approach is investigated for positioning using measurements from a global navigation satellite system that are possibly contaminated by multipath interferences.
Study of the Couple (Reflection Coefficient, K-Nn Rule)

Some sets of parameters, obtained from a one-to-one transformation from AR ones, have successfully been used in Spectral Analysis and Classification. In a previous paper El], a statistical analysis of the cepstral coefficient probability density function (p.d.f.) revealed why, in many cases, the k-NN rule is a necessary tool in this particular representation space. The aim of this paper is to study a further set of parameters : the reflection coefficients.
Skull Conductivity Estimation for EEG Source Localization

A reliable leadfield matrix is needed to solve the magnetoencephalography/electroencephalography (M/EEG) source localization problem. The computation of this matrix requires several physical parameters, including the conductivity of the tissues that compose the subject's head. Since it is not precisely known, we modify a recent Bayesian algorithm to estimate the skull conductivity jointly with the brain activity directly from the M/EEG measurements. Synthetic and real data are used to compare our technique with two optimization algorithms, showing that the proposed method is able to provide results of similar or better quality with the advantage of being applicable in a more general case.
Nonlinear hyperspectral unmixing using Gaussian processes

This paper presents an unsupervised algorithm for nonlinear unmixing of hyperspectral images. The proposed model assumes that the pixel reflectances result from a nonlinear function of the abundance vectors associated with the pure spectral components. We assume that the spectral signatures of the pure components and the nonlinear function are unknown. The first step of the proposed method estimates the abundance vectors for all the image pixels using a Gaussian process latent variable model. The endmembers are subsequently estimated using Gaussian process regression. The performance of the unmixing strategy is compared with state-of-the-art unmixing strategies on synthetic data. An interesting property is its robustness to the absence of pure pixels in the image.
Barankin lower bound for change-points in independent sequences

In this paper, the computation of the Barankin bound (BRB) for an unbiased estimator of the step location for an independent discrete-time signal is proposed. In the derivation of the change-point BRB for different observation vector distributions the usual Gaussian case is first considered. Simulation results show that the BRB provides a reasonable lower bound for the estimated mean square errors (MSE) of the change-point maximum likelihood estimator (MLE).
Nonlinear Unmixing of Hyperspectral Images Using a Generalized Bilinear Model

Nonlinear models have recently shown interesting properties for spectral unmixing. This paper studies a generalized bilinear model and a hierarchical Bayesian algorithm for unmixing hyperspectral images. The proposed model is a generalization not only of the accepted linear mixing model but also of a bilinear model that has been recently introduced in the literature. Appropriate priors are chosen for its parameters to satisfy the positivity and sum-to-one constraints for the abundances. The joint posterior distribution of the unknown parameter vector is then derived. Unfortunately, this posterior is too complex to obtain analytical expressions of the standard Bayesian estimators. As a consequence, a Metropolis-within-Gibbs algorithm is proposed, which allows samples distributed according to this posterior 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.
A reversible-jump mcmc algorithm for estimating the number of endmembers in the normal compositional model application to the unmixing of hyperspectral images

In this paper, we address the problem of unmixing hyperspectral images in a semi-supervised framework using the normal compositional model recently introduced by Eismann and Stein. Each pixel of the image is modeled as a linear combination of random endmembers. More precisely, endmembers are modeled as Gaussian vectors whose means belong to a known spectral library. This paper proposes to estimate the number of endmembers involved in the mixture, as well as the mixture coefficients (referred to as abundances) using a trans-dimensional algorithm. Appropriate prior distributions are assigned to the abundance vector (to satisfy constraints inherent to hyperspectral imagery), the noise variance and the number of components involved in the mixture model. The computational complexity of the resulting posterior distribution is alleviated by constructing an hybrid Gibbs algorithm which generates samples distributed according to this posterior distribution. As the number of endmembers is unknown, the sampler has to jump between spaces of different dimensions. This is achieved by a reversible jump Markov chain Monte Carlo method that allows one to handle the model order selection problem. The performance of the proposed methodology is evaluated thanks to simulations conducted on synthetic data.
An Affine Combination of Two LMS Adaptive Filters—Transient Mean-Square Analysis

This paper studies the statistical behavior of an affine combination of the outputs of two least mean-square (LMS) adaptive filters that simultaneously adapt using the same white Gaussian inputs. The purpose of the combination is to obtain an LMS adaptive filter with fast convergence and small steady-state mean-square deviation (MSD). The linear combination studied is a generalization of the convex combination, in which the combination factor lambda(n) is restricted to the interval (0,1). The viewpoint is taken that each of the two filters produces dependent estimates of the unknown channel. Thus, there exists a sequence of optimal affine combining coefficients which minimizes the mean-square error (MSE). First, the optimal unrealizable affine combiner is studied and provides the best possible performance for this class. Then two new schemes are proposed for practical applications. The mean-square performances are analyzed and validated by Monte Carlo simulations. With proper design, the two practical schemes yield an overall MSD that is usually less than the MSDs of either filter.
Blind Unmixing of Linear Mixtures using a Hierarchical Bayesian Model. Application to Spectroscopic Signal Analysis

This paper addresses the problem of spectral unmixing when positivity and additivity constraints are imposed on the mixing coefficients. A hierarchical Bayesian model is introduced to satisfy these two constraints. A Gibbs sampler is then proposed to generate samples distributed according to the posterior distribution of the unknown parameters associated to this Bayesian model. Simulation results conducted with synthetic data illustrate the performance of the proposed algorithm. The accuracy of this approach is also illustrated by unmixing spectra resulting from a multicomponent chemical mixture analysis by infrared spectroscopy.
A New Decision-Theory-Based Framework for Echo Canceler Control

A control logic has a central role in many echo cancellation systems for optimizing the performance of adaptive filters, while estimating the echo path. For reliable control, accurate double-talk and channel change detectors are usually incorporated to the echo canceler. This work expands the usual detection strategy to define a classification problem characterizing four possible states of the echo canceler operation. The new formulation allows the use of decision theory to continuously control the transitions among the different modes of operation. The classification rule reduces to a low-cost statistics, for which it is possible to determine the probability of error under all hypotheses, allowing the classification performance to be accessed analytically. Monte Carlo simulations using synthetic and real data illustrate the reliability of the proposed method.
Estimation of the degree of polarization in compact polarimetry

The degree of polarization (DoP) has long been recognized as one of the most important parameters characterizing partially polarized electromagnetic waves. This parameter can be effectively used to describe the information content of polarimetric images collected by synthetic aperture radar (SAR) systems. Estimation of DoP is standardly performed using four measurements. In SAR compact polarimetry (CP), however, only two measurements are available. In this paper, we develop maximum likelihood estimators of the DoP, in SAR CP modes, based on only two intensity images. We evaluate and compare the performance of these estimators for different CP modes on RADARSAT-2 polarimetric data, over various terrain types such as urban, vegetation, and ocean.
Joint Bayesian Estimation of Close Subspaces from Noisy Measurements

In this letter, we consider two sets of observations defined as subspace signals embedded in noise and we wish to analyze the distance between these two subspaces. The latter entails evaluating the angles between the subspaces, an issue reminiscent of the well-known Procrustes problem. A Bayesian approach is investigated where the subspaces of interest are considered as random with a joint prior distribution (namely a Bingham distribution), which allows the closeness of the two subspaces to be parameterized. Within this framework, the minimum mean-square distance estimator of both subspaces is formulated and implemented via a Gibbs sampler. A simpler scheme based on alternative maximum a posteriori estimation is also presented. The new schemes are shown to provide more accurate estimates of the angles between the subspaces, compared to singular value decomposition based independent estimation of the two subspaces.
Characterization of clutter heterogeneity and estimation of its covariance matrix

Heterogeneous clutter environments are frequently encountered in radar processing and constitute a main source of performance degradation for most adaptive detection schemes which use the sample covariance matrix (SCM). This degradation is mainly due to the fact the SCM is no longer a consistent estimate of the clutter covariance matrix in the cell under test (CUT). In a recent paper we proposed a knowledge-aided Bayesian framework for such heterogeneous environments, and derived the associated minimum mean-square error (MMSE) estimate of the CUT covariance matrix. Both the degree of heterogeneity and the degree of a priori knowledge were adjusted by known scalar variables denoted as nu and mu. In this paper, we extend these results to the more practical case where these scalars are unknown and have to be estimated together with the CUT covariance matrix. We extend the Gibbs sampler strategy of our previous work to this new problem. This allows us in particular to estimate the degree of heterogeneity nu of the clutter, and hence to characterize the environment. We show that the MSE for estimation of the CUT covariance matrix is only marginally increased compared to the case of known nu and mu. The new scheme is also successfully applied to real radar data where we show that it can distinguish between homogeneous and nonhomogeneous areas in a range-azimuth map.