Caractérisation des images stationnaires par des modèles non gaussiens bidimensionnels à moyenne ajustée

In this paper, four batch least squares linear approaches are presented for identification of non minimum phase bidimensional non Gaussian moving average (MA) models, and a relationship between autocorrelation and cumulant sequences is given. One of the proposed methods is cumulant-based only but the others use both autocorrelations and m-th order cumulants (m. > 2). Three of them are derived from the Brillinger-Rosenblatt’s non linear relation by using the Tugnait’s closed-form solution. Also, we generalize to m-th order cumulants the 2-D version of Giannakis-Mende’s approach. By simulations, we test and compare the Tugnait’s closed-form solution and the proposed methods, and we evaluate the performance of our relationship in noisy environment. Finally, we propose to characterize textured images by a 2-D ma model witch will be identified using our approaches in noisy and free noise cases.     

Convergence-optimized, higher order vector finite elements formicrowave simulations

We introduce a general class of higher order parameter-dependent Whitney elements, unlike previous approaches that resulted in specific element definitions. All elements of this kind provide the same solution, but their convergence properties may be significantly different. The most essential fact, though, is the introduction of an optimization procedure, which reveals the existence of an optimal, with respect to convergence, element. The produced second order elements are tested in both two-dimensional (2-D) and three-dimensional (3-D) microwave simulations     

Broadband analytical magnetoquasistatic electromagnetic induction solution for a conducting and permeable spheroid

We use a hybrid model including asymptotic expressions of the spheroidal wave functions (SWFs) to obtain a reliable broadband solution for the electromagnetic induction (EMI) response from a conducting and permeable spheroid. We obtain this broadband response, valid in the magnetoquasistatic regime from zero to hundreds of kilohertz, by combining three different techniques, each applicable over a different frequency range. At low frequencies, the exact analytical solution is used. At midrange frequencies, asymptotic expressions for the angular and radial SWFs are incorporated into the exact solution in order to maintain a stable solution for the induced magnetic field. At higher frequencies, a small penetration approximation (SPA) solution is used when the SPA solution approaches the asymptotically assisted solution to within some predefined tolerance. Validation of this combined technique is accomplished through the comparison of the induced magnetic field predicted by our model to both a finite element/boundary integral (FE-BI) numerical solution and experimental data from various spheroids taken by an ultrawideband EMI instrument.     

AN IMPROVEMENT OF THE RECURSIVE CLOSED-FORM ALGORITHM FOR NON-MINIMUM PHASE FIR SYSTEM IDENTIFICATION

An improved algorithm which is based on the recursive closed-form algorithm fornon-minimum phase FIR system identification via higher order statistics is presented.In order toincrease the parametric estimation accuracy,the improved algorithm uses the optimal iterativemethod to seek the nonlinear least-square solution.Finally,the simulation examples are alsogiven.     

Automatic target recognition of radar HRRP based on high order central moments features

The paper addresses the problem of target recognition using High-resolution Radar Range Profiles (HRRP). A novel approach of feature extraction and dimension reduction based on extended high order central moments is proposed in order to reduce the dimension of range profiles. Features extracted from radar HRRPs are normalized and smoothed, and then comparative analysis of the similar approaches is done. The range profiles are obtained by step frequency technique using the two-dimensional backscatters distribution data of four different aircraft models. The template matching method by nearest neighbor rules, which is based on the theory of kernel methods for pattern analysis, is used to classify and identify the range profiles from four different aircrafts. Numerical simulation results show that the proposed approach can achieve good performance of stability, shift independence and higher recognition rate. It is helpful for real-time identification and the engineering implements of automatic target recognition using HRRP. The number of required templates could be reduced considerably while maintaining an equivalent recognition rate.     

(Almost) periodic moving average system identification using higherorder cyclic-statistics

This article addresses the problem of (almost) periodic moving average (APMA) system identification. Two new normal equations relating the coefficients of an APMA system and the time-varying higher order cumulants of the measurements are established, from which two new linear algebraic algorithms are presented for system parameter estimation. In addition, a new singular value decomposition (SVD) based algorithm is proposed for determining the system order. Simulation examples are given to demonstrate the performance of these new approaches     

Improved methods for the blind system identification using higherorder statistics

It is demonstrated by a detailed analysis of blind system identification that under specific system configurations, a recently published least-squares algorithm shows a poor convergence behavior, especially if the system order is overdetermined. To overcome these problems, a supplementary condition is introduced that guarantees proper convergence in most cases. An alternative approach for the blind identification of mixed-phase systems, the so-called cumulant zero-matching method, is presented. In this approach, the solution of a set of nonlinear equations, which is necessary in the least-squares method, is replaced by the calculation of zeros of polynomials. The main advantage over the least-squares solution is that overdetermination of the system order is rather harmless, since it only results in additional zeros in the origin of the z-plane. The different methods for system identification presented are illustrated by simulation results     

Linear-quadratic system identification with completed frequencydomain region of support

In general, aliasing exists in the output of discrete-time nonlinear systems due to the higher order harmonics and intermodulation among inputs. If the input is allowed to cover the full range from DC to half the sample rate, the aliased components in the output must be taken into account for accurate system identification. We have extended a known system identification method to account for these aliased terms by completing the region of support for the quadratic transfer function. We also present a method for preventing a form of time domain aliasing. Together, these approaches can lead to significantly improved system characterization with less data as compared to current techniques     

基于高阶统计量的系统的盲辨识

通过对二阶统计量和高阶统计量的系统辨识方法的比较,重点介绍基于高阶统计量的系统盲辨识,分FIR和IIR两种情况简要描述了在这两种结构中实现系统盲辨识的方法,同时作为盲辨识的一个重要方面,也对系统的时延估计进行了讨论。随着通信和现代信号处理技术的发展,将高阶统计量的观点应用于MIMO系统的辨识已成为一个研究热点。     

Blind MIMO system identification based on cumulant subspace decomposition

Blind identification of multiple-input multiple-output (MIMO) linear systems can be achieved by utilizing higher order statistics of the output signals. We study the blind identification of MIMO systems whose inputs are mutually independent, temporally white, non-Gaussian source signals. Based on sub-space analysis, we develop a new linear batch algorithm to identify MIMO systems from the common space of a set of fourth-order cumulant matrices of the channel outputs. Given knowledge of the channel orders, the identifiability conditions required by the proposed algorithm are properly established. Like most subspace-based approaches, this new algorithm remains sensitive to channel order overestimation. Simulation results illustrate its performance for various channel models.     

HOS-based nonparametric and parametric methodologies for machinefault detection

A framework for the detection and identification of machine faults through vibration measurements and higher order statistics (HOS) analysis is presented. As traditional signal processing techniques are based on the nonparametric magnitude analysis of vibration signals, in this paper, two different state-of-the-art HOS-based methods, namely, a nonparametric phase-analysis approach and a parametric linear or nonlinear modeling approach are used for machine fault diagnostic analysis. The focus of this paper is on the application of the techniques, not on the underlying theories. Each technique is described briefly and is accompanied by an experimental discussion on how it can be applied to classify the synthetic mechanical and electrical faults of induction machines compared with their normality. Promising results were obtained which show that the presented methodologies are possible approaches to perform effective preventive maintenance in rotating machinery     

Multichannel ARMA processes

Parametric modeling of multichannel time series is accomplished by using higher (than second) order statistics (HOS) of the observed nonGaussian data. Cumulants of vector processes are defined using a Kronecker product formulation, and consistency of their sample estimators is addressed. Identifiability results in connection with the HOS-based parameter estimation of causal and noncausal multivariate ARMA processes are established. Estimates of the parameters of causal ARMA models are obtained as the solution to a set of linear equations, whereas those of noncausal ARMA models are obtained as the solution to a cumulant matching algorithm. Conventional approaches based on second-order statistics can identify a multichannel system only to within post multiplication by a unimodular matrix. HOS-based methods yield solutions that are unique to within post-multiplication by an (extended) permutation matrix; additionally, the multiminimum phase assumption can be relaxed, and the observations may be contaminated with colored Gaussian noise. Frequency-domain methods for nonparametric system identification are discussed briefly. Simulations results validating the multichannel parameter estimation algorithms are provided     

An adaptive algorithm for antenna array low-rank processing incellular TDMA base stations

A new adaptive algorithm for blind interference rejection and multipath mitigation is studied and applied to antenna array processing in the reverse channel of a time-division multiple-access (TDMA) cellular communication system. The method is based on higher order statistics (HOS) processing of the baseband vector samples at the antenna array output. The similarity between the cumulant-based solution and the standard multivariable least-squares solution is exploited to derive an efficient adaptive algorithm based on a low-rank processing architecture. The algorithm exhibits good tracking and enhanced identification capability with respect to traditional least-squares methods     

A New Look to Multichannel Blind Image Deconvolution

The aim of this paper is to propose a new look to MBID, examine some known approaches, and provide a new MC method for restoring blurred and noisy images. First, the direct image restoration problem is briefly revisited. Then a new method based on inverse filtering for perfect image restoration in the noiseless case is proposed. The noisy case is addressed by introducing a regularization term into the objective function in order to avoid noise amplification. Second, the filter identification problem is considered in the MC context. A new robust solution to the degradation matrix filter is then derived and used in conjunction with a total variation approach to restore the original image. Simulation results and performance evaluations using recent image quality metrics are provided to assess the effectiveness of the proposed methods.     

AF中继下行链路系统的能效资源分配方案

为最大化放大转发中继系统的下行链路总能效,结合系统的电路功率,提出了一种基于能效的中继选择和功率分配联合方案.为降低复杂度,采用分步式次优化方案,利用虚拟直传信道增益得到中继选择方法,并将中继系统转换为单跳系统;然后利用凸规划得到最优功率分配.此外,为适应不同的用户分布,提出联合小区呼吸机制的分配方案.仿真结果表明,所提次优联合方案逼近最优值,联合小区呼吸的方案可适应不同的用户分布并进一步提升能效.     

Advances in Lee–Schetzen Method for Volterra Filter Identification

This paper concerns the identification of nonlinear discrete causal systems that can be approximated with the Wiener–Volterra series. Some advances in the efficient use of Lee–Schetzen (L–S) method are presented, which make practical the estimate of long memory and high order models. Major problems in L–S method occur in the identification of diagonal kernel elements. Two approaches have been considered: approximation of gridded data, with interpolation or smoothing, and improved techniques for diagonal elements estimation. A comparison of diagonal elements estimated, with different methods has been shown with extended tests on fifth order Volterra systems.First Online Version Published in July, 2005     

Direct estimation of blind zero-forcing equalizers based onsecond-order statistics

Most existing zero-forcing equalization algorithms rely either on higher than second-order statistics or on partial or complete channel identification. We describe methods for computing fractionally spaced zero-forcing blind equalizers with arbitrary delay directly from second-order statistics of the observations without channel identification. We first develop a batch-type algorithm; then, adaptive algorithms are obtained by linear prediction and gradient descent optimization. Our adaptive algorithms do not require channel order estimation, nor rank estimation. Compared with other second-order statistics-based approaches, ours do not require channel identification at all. On the other hand, compared with the CMA-type algorithms, ours use only second-order statistics; thus, no local convergence problem exists, and faster convergence can be achieved. Simulations show that our algorithms outperform most typical existing algorithms     

Efficient singular element for finite element analysis of quasi-TEMtransmission lines and waveguides with sharp metal edges

The FEM presents a slow rate of convergence when it is used in the analysis of quasi-TEM transmission lines or homogeneous waveguides with field singularities. In order to improve this drawback, mesh techniques or vector elements that cope with the singularities can be used. A different solution is to employ scalar singular elements although, most of those that have been used are only compatible with first-order ordinary elements or can only be used with field singularities of order O(r^-1/2) and O(r^-1/3). In this paper, we present an improvement on the rate of convergence of FEM by employing a scalar singular element, which can be utilized for any order of singularity, is compatible with quadratic or higher order standard elements and is also easy to implement in standard finite element codes. Several transmission lines and waveguides with sharp metal edges have been analysed with a reduced number of degrees of freedom that compares well with other FEM approaches. We also show that electromagnetic fields computed using the proposed singular element have very good agreement with the ones theoretically expected from the singular edge condition     

Cumulant-based blind identification of linearmulti-input-multi-output systems driven by colored inputs

The blind identification problem of a linear multi-input-multi-output (MIMO) system is widely noticed by many researchers in diverse fields due to its relevance to blind signal separation. However, such a problem is ill-posed and has no unique solution. Therefore, we can only find a solution of the problem within an equivalence class. We address the blind identification problem of the linear MIMO system driven by unobservable colored inputs using higher order statistics (HOS), particularly the fourth-order cumulants, of the outputs, where the unobservable inputs are mutually independent but temporally colored linear processes. We first define the set, which is denoted by S, of stable scalar transfer functions and then define the notion of a generalized permutation matrix (which is abbreviated by a g-matrix) over S. Then, it is shown that the transfer function matrix of an unknown system is identified only up to post-multiplication by a g matrix. This result is applied to identifying FIR systems for blind signal separation