Showing papers by "Karim Abed-Meraim published in 2005"

A weighted linear prediction method for near-field source localization

Abstract: This paper proposes a low computational cost method for the near-field narrowband source localization problem, which does not require multidimensional search or high-order statistics. The proposed method is based on the second-order statistics (SOS) of the outputs of a uniform linear array (ULA). More precisely, the range and angle parameters are estimated through a weighted linear prediction (LP) algorithm applied to a properly chosen array output correlation sequence. Detailed performance analysis and derivation of the optimal weightings are provided. Simulation results are finally presented to validate the theoretical analysis results and to assess the performance of the proposed method.

Separating more sources than sensors using time-frequency distributions

Abstract: We examine the problem of blind separation of nonstationary sources in the underdetermined case, where there are more sources than sensors. Since time-frequency (TF) signal processing provides effective tools for dealing with nonstationary signals, we propose a new separation method that is based on time-frequency distributions (TFDs). The underlying assumption is that the original sources are disjoint in the time-frequency (TF) domain. The successful method recovers the sources by performing the following four main procedures. First, the spatial time-frequency distribution (STFD) matrices are computed from the observed mixtures. Next, the auto-source TF points are separated from cross-source TF points thanks to the special structure of these mixture STFD matrices. Then, the vectors that correspond to the selected auto-source points are clustered into different classes according to the spatial directions which differ among different sources; each class, now containing the auto-source points of only one source, gives an estimation of the TFD of this source. Finally, the source waveforms are recovered from their TFD estimates using TF synthesis. Simulated experiments indicate the success of the proposed algorithm in different scenarios. We also contribute with two other modified versions of the algorithm to better deal with auto-source point selection.

A new trilateration method to mitigate the impact of some non-line-of-sight errors in TOA measurements for mobile localization

Abstract: In this contribution, a new mobile station (MS) localization method is provided using time of arrival (TOA) measurements, in the UMTS-FDD downlink. Contrary to the usual trilateration algorithms, the proposed method takes into account possible large TOA error measurements caused by non-line-of-sight (NLOS) and near-far-effect (NFE). To this end, the new method measures the 'coherence' between the TOA estimates and allows the mobile to select the three most reliable measures among the whole available TOA measurements. Realistic simulations show the accuracy improvement provided by the proposed algorithm over a simple trilateration.

Blind separation of impulsive alpha-stable sources using minimum dispersion criterion

Abstract: This letter introduces a novel blind source separation (BSS) approach for extracting impulsive signals from their observed mixtures. The impulsive signals are modeled as real-valued symmetric alpha-stable (S/spl alpha/S) processes characterized by infinite second- and higher-order moments. The proposed approach uses the minimum dispersion (MD) criterion as a measure of sparseness and independence of the data. A new whitening procedure by a normalized covariance matrix is introduced. We show that the proposed method is robust, so-named for the property of being insensitive to possible variations in the underlying form of sampling distribution. Algorithm derivation and simulation results are provided to illustrate the good performance of the proposed approach. The new method has been compared with three of the most popular BSS algorithms: JADE, EASI, and restricted quasi-maximum likelihood (RQML).

Damped and delayed sinusoidal model for transient signals

Abstract: In this work, we present the Damped and Delayed Sinusoidal (DDS) model: a generalization of the sinusoidal model. This model takes into account an angular frequency, a damping factor, a phase, an amplitude, and a time-delay parameter for each component. Two algorithms are introduced for the DDS parameter estimation using a subband processing approach. Finally, we derive the Crame/spl acute/r-Rao Bound (CRB) expression for the DDS model and a simulation-based performance analysis in the context of a noisy fast time-varying synthetic signal and in the audio transient signal modeling context.

Investigation of the channel estimation error on MIMO system performance

Abstract: Multi-Input Multi-Output (MIMO) wireless communication systems provide large capacity allowing high data rates transmission. However, this huge increase of the capacity requires perfect channel knowledge at the receiver. In this paper, we analyze the effect of imperfect channel knowledge on the achievable rates. More precisely, we express a lower bound on the capacity as a function of the channel Cramer-Rao bound (CRB). In previous works, we made the calculation and comparison of CRB for different channel estimation schemes [1] and [2]. This allows us to make comparison of channel achievable rates for different context under various data and pilot design assumptions.

Mobile localization using subspace tracking

Abstract: Time-of-arrival (TOA) and direction-of-arrival (DOA) based methods are essential for mobile positioning in UMTS networks. Because of the presence of closely spaced multipaths, timing errors will occur when standard correlation based estimation methods are applied. To mitigate this problem, we propose here to use first a rough estimate of the desired TOA followed by T-MUSIC algorithm for a refinement of this estimate. The latter algorithm is implemented using a modified version of the adaptive sub-space tracking algorithm OPAST (orthogonal projection approximation subspace tracking) for the extraction and tracking of the principal subspace of a positive Hermitian covariance matrix. Computer simulations prove the numerical stability of the modified OPAST and the good estimation & tracking performance of the proposed method in typical propagation environments

Blind source separation of noisy mixtures using a semi-parametric approach with application to heavy-tailed signals

Abstract: In this paper, we propose a new semi-parametric approach for blind source separation (BSS) of noisy mixtures with application to heavy-tailed signals. The semi-parametric statistical principle is used to formulate the BSS problem as a maximum likelihood (ML) estimation. More precisely, this approach consists of combining the logspline model for sources density approximation with a stochastic version of the EM algorithm for mixing matrix estimation. The proposed method is truly blind to the particular underlying distribution of the mixed signals and performs simultaneously the estimation of the unknown probability density functions (pdf) of the source signals and the estimation of the mixing matrix. The application of logspline density approximation also enables the algorithm to be robust to modelization errors of the sources. In addition, it is robust against outliers and impulsive effect. Computer simulations are provided to illustrate the effectiveness of the proposed separation method comparatively with classical ones.

Fast algorithms for minor component analysis

Abstract: In this paper, we propose new adaptive algorithms for the extraction and tracking of the least (minor) eigenvectors of a positive Hermitian covariance matrix. The proposed algorithms are said fast in the sense that their computational cost is of order O(np) flops per iteration where n is the size of the observation vector and p

Blind equalization and frequency offset estimation in OFDM systems exploiting guard interval redundancy

Abstract: We introduce a new low-complexity equalizer for OFDM systems, which in the presence of a guard interval, utilizes existing redundancy in the time domain to completely eliminate intersymbol-interference. We prove that the usage of this guard interval redundancy is sufficient to obtain a zero-forcing time domain equalizer of the channel. The main advantage of this approach resides in its simplicity as the equalizer coefficients are estimated using a second-order least square fitting (LSF). In addition, to fully exploit the data information, we propose to use the previous LSF based equalizer in conjunction with a constant modulus algorithm (CMA). This equalizer is then adapted to take into account the frequency offset and hence allows the joint mitigation of the ISI and frequency offset effects.

Underdetermined blind source separation of audio sources in time-frequency domain

Abstract: This paper considers the blind separation of audio sources in the underdetermined case, where we have more sources than sensors. A recent algorithm applies time-frequency distributions (TFDs) to this problem and gives good separation performance in the case where sources are disjoint in the time-frequency (TF) plane. However, in the non-disjoint case, the reconstruction of the signals requires some interpolation at the intersection points in the TF plane. In this paper, we propose a new algorithm that combines the abovementioned method with subspace projection in order to explicitly treat non-disjoint sources. Another contribution of this paper is the estimation of the mixing matrix in the underdetermined case.

A Weighted Linear Prediction Method for

Abstract: This paper proposes a low computational cost method for the near-field narrowband source localization problem, which does not require multidimensional search or high-order statistics. The proposed method is based on the second-order statistics (SOS) of the outputs of a uniform linear array (ULA). More precisely, the range and angle parameters are estimated through a weighted linear prediction (LP) algorithm applied to a properly chosen array output correlation sequence. Detailed performance analysis and derivation of the optimal weightings are provided. Simulation results are finally presented to validate the theoretical analysis results and to assess the performance of the proposed method.

Deterministic techniques for multichannel blind image deconvolution

Abstract: In this paper, we address four deterministic methods for blind multichannel identification in a blind image restoration fr amework. These methods are: SubSpace method (SS), Minimum Noise Subspace (MNS) and Symmetric MNS (SMNS) methods, Cross Relation method (CR) and Least Squares Smoothing method (LSS). The latter is a new method that is introduced, here, for the first time, as an extension, from the 1-D to the 2-D case, of the least squares method by L. Tong et al. (1999). For each method, we detail its basic principle and provide a summary of its corresponding algorithm. In the noise free case, all the methods developed here offer a perfect channel identification. In the noisy case, these methods hav e a different behavior and their performance are compared in terms of channel identification by means of MSE.

A new blind adaptive MMSE equalizer for MIMO systems

Abstract: In this paper, we consider the problem of blind equalization of MIMO systems. In a recent work a blind MMSE equalizer robust to channel order over-estimation errors was proposed for SIMO systems. In this work, we extend this method and propose a new MMSE algorithm that presents better robustness and better tracking performances. Moreover, the proposed MMSE technique has been extended here to the MIMO case. Simulation results are then given to illustrate the effectiveness of our MMSE algorithms

Blind separation of audio sources using modal decomposition

Abstract: This paper introduces new algorithms for the blind separation of audio sources using modal decomposition. Indeed, audio signals and, in particular, musical signals can be well approximated by a sum of damped sinusoidal (modal) components. Based on this representation, we propose a two steps approach consisting of a signal analysis (extraction of the modal components) followed by a signal synthesis (pairing of the components belonging to the same source) using vector clustering. For the signal analysis, two algorithms are considered and compared: namely the EMD (Empirical Mode Decomposition) algorithm and a parametric estimation algorithm using ESPRIT technique. A major advantage of the proposed method resides in its ability to separate more sources than sensors. Simulation results are given to compare and assess the performances of the proposed algorithms.

Blind Equalization in OFDM Systems Exploiting Guard Interval Redundancy

Abstract: We introduce a new low-complexity equalizer for OFDM systems, which in the presence of a guard interval, utilizes existing redundancy in the time domain to completely eliminate inter-symbol-interference. We prove that the usage of this guard interval redundancy is sufficient to obtain a zero-forcing time domain equalizer of the channel. The main advantage of this approach resides in its simplicity as the equalizer coefficients are estimated using a second-order least square fitting (LSF). In addition, to fully exploit the data information, we propose to use the previous LSF based equalizer in conjunction with a constant modulus algorithm (CMA). This would help to refine the equalizer estimation, especially for short data lengths, and to improve the system performance. Several implementations are proposed and compared through simulation experiments

On the performance of space time transmit diversity in the downlink of W-CDMA with and without equalization [3G communication applications]

Abstract: In this paper, we discuss the performance of space time transmit diversity (STTD) in the downlink of DS-CDMA over frequency-selective fading channels. We consider two kinds of receiver: the RAKE receiver and the chip-level MMSE equalizer-based receiver. These two receivers comparison turns out to be a very difficult task because their output signal to interference plus noise ratios (SINRs) depend in a complex way on the spreading and scrambling codes. To obtain tractable expressions, we study the SINRs in the asymptotic regime, i.e. we suppose that the spreading factor and the number of users both tend to infinity while their ratio remains constant. We further suppose that the code matrix is a random matrix obtained by multiplying a random scrambling code by a Walsh-Hadamard matrix. Under these conditions, the SINRs of the two receivers tend to deterministic values. We compare the asymptotic SINRs and draw some conclusions about the effect of the channel transfer function on the performance. Simulation results show that the asymptotic results allow us to predict the performance of real life systems like the UMTS-FDD.

Robust quadratic time-frequency distributions for the analysis of mulicomponent FM signals in heavy-tailed noise

Abstract: We consider the problem of instantaneous frequency estimation of multicomponent frequency-modulated signals corrupted by additive heavy-tailed noise. For that, a new time-frequency distribution, named the robust modified B-distribution (R-MBD), is developed as a generalization of the robust minimax M-estimates to handle such signals. We show that this representation outperforms the robust polynomial Wigner-Ville distribution (r-PWVD) in term of high resolution for this class of non-stationary signals. The proposed approach is compared to the higher-order ambiguity function (HAF) algorithm, for the instantaneous frequency estimation of a multicomponent signal. Computer simulations show the superiority of the proposed algorithm over the HAF

Semi-Blind Stochastic Maximum Likelihood for Frequency Selective MIMO Channels

Abstract: We propose a semi-blind stochastic maximum likelihood (SML) estimator for frequency selective multi-input multi-output (MIMO) channel. To resolve the optimization problem we use the expectation-maximization (EM) algorithm. We first propose a hidden Markov model (HMM) for the MIMO frequency selective channel which gives us a formulation of the EM algorithm by introducing forward and backward parameters. Previously, semi-blind SML methods were provided for time-multiplexed pilot and data symbols. In this work, the semi-blind SML is proposed for the embedded pilot scheme which is attractive for its spectral efficiency. We also compare the semi-blind SML for the embedded pilot scheme with the blind SML and show the benefit of using semi-blind SML over blind SML

Blind separation of audio sources convolutive mixtures using parametric decomposition

Abstract: This paper introduces new algorithms for the blind separation of audio sources of instantaneous and convolutive mixtures using modal decomposition. Indeed, audio signals and, in particular, musical signals can be well approximated by a sum of damped sinusoidal (modal) components. Based on this representation, we propose a two steps approach consisting of a signal analysis (extraction of the modal components) followed by a signal synthesis (pairing of the components belonging to the same source). For the signal analysis, we consider a parametric estimation algorithm using ESPRIT technique. A major advantage of the proposed method resides in its ability to separate more sources than sensors in the instantaneous mixture case. Simulation results are given to assess the performance of the proposed algorithm.

Time-Frequency Signal Processing for Wireless Communications

Abstract: This chapter is intended to relate recent advances in the field of time–frequency signal processing (TFSP) to the need for further capacity of wireless communications systems. It first presents, in a brief and heuristic approach, the fundamentals of TFSP. It then describes the TFSP-based methodologies that are used in wireless communications with special emphasis on spread-spectrum techniques and time–frequency array processing. Topics discussed include channel modeling and identification, estimation of scattering function, interference mitigation, direction of arrival estimation, time–frequency MUSIC, and time–frequency source separation. Finally, other emerging applications of TFSP to wireless communications are discussed.

Damped and delayed sinuosidal model for transient modeling

Abstract: In this work, we present the Damped and De- layed Sinusoidal (DDS) model, a generalization of the sinu- soidal model. This model takes into account an angular fre- quency, a damping factor, a phase, an amplitude and a time- delay parameter for each component. Two algorithms are introduced for the DDS parameter estimation using a sub- band processing approach. Finally, we derive the Cramer- Rao Bound (CRB) expression for the DDS model and a simulation-based performance analysis in the context of a noisy fast time-varying synthetic signal and in the audio transient signal modeling context.

Séparation aveugle sous-déterminée de sources audio par la méthode EMD (Empirical Mode Decomposition)

Abstract: Dans le cadre de la separation aveugle de sources, nous montrons dans cet article comment effectuer la separation de melanges instantanes de sources audio en utilisant une methode basee sur l'algorithme de Decomposition Modale Empirique (ou EMD, pour Empirical Mode Decomposition). Cette approche nous permet, en particulier de traiter le cas sous determine (c'est a dire le cas ou l'on a moins de capteurs que de sources). L'approche EMD se base sur le fait que les signaux audio (et particulierement les signaux musicaux) peuvent etre bien modelises localement par une somme de signaux periodiques. Ces signaux seront donc decomposes en utilisant l'algorithme EMD et recombines par classification suivant leurs directions spatiales regroupant ainsi les composantes de chacune des sources. Nous presenterons quelques resultats de simulation qui permettent d'evaluer les performances de cette nouvelle methode.

Delayed exponential fitting by best tensor rank-(R/sub 1/, R/sub 2/, R/sub 3/) approximation

Abstract: We present a subspace-based scheme for the estimation of the poles (angular-frequencies and damping-factors) of a sum of damped and delayed sinusoids. In our model each component is supported over a different time frame, depending on the delay parameter. Classical subspace based methods are not suited to handle signals with varying time-support. In this contribution, we propose a solution based on the best rank-(R/sub 1/, R/sub 2/, R/sub 3/) approximation of a partially structured Hankel tensor on which the data are mapped. We show, by means of an example, that our approach outperforms the current tensor and matrix-based approaches in terms of the accuracy of the damping parameter estimates.

Investigations on contrast functions for blind source separation based on non-Gaussianity and sparsity measures

Abstract: In this paper, we provide a systematic method to construct contrast functions through the use of sub- or super- additive functionals. The used sub- or super-additive functionals are applied to the distributions of the extracted sources to quantify the degree of non-Gaussianity or sparsity. In this work, we assume a completely blind scenario where one knows only the observations and the existence of at most one Gaussian independent component in the mixture. However, there is no a priori information about the mixing matrix nor about the source density. Some practical examples of useful contrast functions are introduced and discussed in order to illustrate the usefulness of the proposed approach.

Method for receiving a cdma interference suppression signal and a corresponding receiver

Abstract: The invention relates to a method for receiving a CDMA signal comprising at least two channels each of which corresponds to a distinct source signal whose at least one channel is of interest for extraction, said received signal being transmitted by a multibeam transmission channel. The inventive reception method consists in equalising (60) the received signal by transmitting a single equalised signal, estimating (61-66) an interference induced by the source signal transmitted through al least one channel other than said channel of interest in order to obtain a more representative signal of said channel of interest than the received signal, in processing said representative signal for the channel of interest, each of said channels being obtained by multiplying said corresponding source signal with a predetermined asymmetry code, the asymmetry code used for said at least one other channel is estimated at random according to at lest one predetermined criterion during said estimation of the corresponding source signal.

Code division multiple access signal receiving method for universal mobile telecommunication system-frequency division duplex, involves estimating interference induced by source signal transmitted through channels

Abstract: The method involves equalizing a received signal (30) An interference induced by a source signal transmitted through channels other than a channel of interest, is estimated from the equalized signal in order to obtain a representative signal of the channel of interest An asymmetry code for the channels is estimated at random according to predetermined criteria during the estimation of the corresponding source signal An independent claim is also included for a code division multiple access (CDMA) signal receiver

Estimation des paramètres de propagation d'un canal MIMO par l'algorithme ESPRIT-Unitaire multidimentionnel

Abstract: Resume – Dans ce travail, nous proposons une extension de l’algorithme ESPRIT-Unitaire au cas multidimensionnel (6D) afin d’estimer les differents parametres du canal radio regi par un modele de propagation en rayons. Cette methode nous permet d’estimer pour un systeme MIMO (Multi-Input Multi-Output) tous les parametres utiles des rayons, i.e. les angles d’emission et de reception (azimuth et elevation), les retards, les dopplers et les amplitudes complexes. On etablit aussi la robustesse de cette methode vis a vis d’une surestimation du nombre de trajets ’effectifs’ du canal. Cette robustesse nous permet, en particulier, de nous affranchir de l’etape ’delicate’ de l’estimation du nombre de trajets. Nous presentons quelques resultats de simulation permettant d’evaluer l’efficacite de cette nouvelle methode et d’illustrer sa robustesse vis a vis de la meconnaissance du nombre de trajets.