Singular spectrum analysis (SSA) is a nonparametric spectral estimation method that decomposes time series signals into interpretable components. With the rise of big time series, the demand for effective and scalable SSA techniques has become increasingly urgent. In this paper, we propose a novel multiway extension of SSA, called higher-order multivariate SSA (HO-MSSA), specifically designed for multivariate and multichannel time series signal analysis via tensor decomposition. HO-MSSA utilizes time-delay embedding and tensor singular value decomposition to transform multichannel time series signals into trajectory tensors, which are then decomposed into elementary components in the Fourier domain, rather than the time domain as in traditional SSA methods. These components are grouped into disjoint subsets using spectral clustering, enabling the reconstruction of the underlying source signals. Experimental results demonstrate that HO-MSSA outperforms state-of-the-art SSA methods in various biomedical applications, including electromyography (EMG), electrocardiography (ECG), and electroencephalogram (EEG) signals. • A novel tensor-based multivariate singular spectrum analysis is introduced. • A new time embedding technique embeds time series into a higher dimension. • Tensor SVD is used to factorize the trajectory tensor of time series. • Spectral clustering detects and groups the underlying time series components.

This paper proposes a semi-blind approach for near-field channel estimation and 3D source localization for Extremely Large Antenna Arrays. It considers Uniform Planar Arrays and Cross Arrays as particular cases. Unlike conventional Least-Squares estimators relying solely on predefined pilots, the proposed method exploits unknown data symbols within a subspace framework. Since it does not require any prior knowledge of the propagation model, our proposal achieves improved accuracy and robustness. To reduce computational complexity, a Divide-and-Conquer strategy is adopted, enabling fast and parallelizable subspace estimation. In a multipath single-user scenario with Inter-Symbol Interference, localization parameters (angles and range) are recovered from the first channel tap via a low-complexity least-squares fitting, assuming a line-of-sight path. The extension to Cross Arrays provides a simple specialization of the Uniform Plannar Array approach while drastically reducing the computational load. Simulation results confirm that the proposed method achieves accurate channel estimation and 3D localization with substantial complexity reduction as compared to the state of the art.

This work introduces a new semi-blind multi-modulus approach for joint source separation and carrier frequency offset (CFO) estimation in Multiple-Input Multiple-Output Orthogonal Frequency Division Multiplexing communications. A hybrid semi-blind cost function based on the Least Squares (LS) and Multi-Modulus (MM) is designed for source recovery and CFO estimation. An alternating gradient descent optimization method is utilized to iteratively minimize the cost function along the search direction. Furthermore, an efficient initialization procedure is introduced to reduce the algorithm's complexity and ensure its convergence. The simulation results show that our method performs very well in terms of CFO estimation and data recovery.

This paper introduces TenSOFO, a novel tensor-based method specifically designed for blind source separation (BSS). Ten-SOFO presents a new efficient alternating direction method of multipliers framework, allowing for simultaneous decomposition of two symmetric third-order tensors under the individual differences in scaling (INDSCAL) format. By establishing a fundamental link between joint INDSCAL decomposition and BSS using second and fourth order statistics, TenSOFO proves to be effective for BSS. The performance of TenSOFO is evaluated in both joint INDSCAL decomposition and BSS tasks, showcasing its remarkable accuracy and potential applications.

In this paper, we investigate the problem of convolutive blind source separation (BSS) via tensor decomposition. A fundamental link between convolutive BSS and block-term decomposition (BTD) is established, forming the basis for our novel tensor-based convolutive BSS method, namely TCBSS. Specifically, the proposed method offers a new effective approach for factorizing tensors under the BTD format where the loading factors are constrained to be identical. By leveraging second-order statistics of data observations, we construct a third-order tensor by stacking covariance matrices at different time lags, and then, apply TCBSS to identify the mixing process. Experimental results demonstrate the robust performance of TCBSS in addressing both BTD and convolutive BSS tasks, particularly when dealing with electromyography (EMG) signal decomposition.

Gearbox condition monitoring and quality surveillance are crucial techniques to ensure safe and cost-efficient machine operations. In condition monitoring, the interpretation of the different vibration spectrum elements is still an open question, many works show that some predefined vibration models are improper to explain the spectrum contents. In this paper, we investigate a method to identify the mixture model that describes a single-stage planetary gearbox vibration to properly interpret the vibration spectrum. Our method is based on neural network-based symbolic regression, a so-called equation learner that describes the vibration model based on prior knowledge about the planetary gearbox rotation frequencies. The method employs an end-to-end differentiable feed-forward network trained with sparsity regularization that promotes an interpretable and concise expression for the vibration measurement. With this, the obtained model contributes to increasing the effectiveness of vibration-based condition monitoring in the planetary gearbox with proper separation of the elementary vibration sources. Our proposed approach yields promising results in modeling and sources estimation based on simulated data, even at low SNRs.

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Like the Joint Diagonalization of a unique set of matrices, Generalized Joint Diagonalization is an algebraic problem encountered in different applications such as data fusion and blind source separation. This letter proposes a new generalized unitary joint diagonalization approach based on the Jacobi iterative scheme using Givens rotations and simplified criterion, introducing three approximations. These approximations allowed us to reach a simultaneous estimation of different parameters. The first appears in the simplified criterion composed of entries doubly affected by the Givens rotations. The second approximation is in the Givens parameter using a small amplitude angle. The last approximation resides in keeping only the first order of transformed entries. Numerical experiments, including examples of joint blind audio source separation, are provided. The results show the effectiveness of the developed algorithm as compared to existing ones. The simultaneous estimation of different Givens rotations improves the algorithm's computation complexity and convergence rate.

This paper deals with the problem of blind and semi-blind subspace-based channel estimation, when considering MIMO-OFDM communications systems. The proposed solution offers a reduced computational complexity, mainly by a factor of the number of subcarriers, while guaranteeing accurate channel estimation as compared to state-of-the-art techniques. By exploiting the orthogonality property of the OFDM modulation, covariance matrix and noise subspace are estimated for each subcarrier in a parallel scheme, then a global cost function is minimized to obtain channel coefficients estimates. Besides, conditions for channel identifiability as well as the minimum number of subcarriers to be used for the uniqueness of the solution are investigated with various numerical simulations to corroborate our analysis.

We studied the problem of robust subspace tracking (RST) in contaminated environments. Leveraging the fast approximated power iteration and α-divergence, a novel robust algorithm called αFAPI was developed for tracking the underlying principal subspace of streaming data over time. αFAPI is fast and it outperforms many RST methods while only having a low complexity linear to the data dimension. Some experiments were conducted to illustrate the performance of αFAPI.

In digital communication, nonlinearity is a frequent source of signal and channel distortion. Such distortions often need equalization techniques or devices to correct them. An equalization technique for recovering nonlinear multichannel signals in convolutive mixture is presented in this article. The proposed work uses the mutually referenced equalization technique to estimate the equalizer and the transmitted signal from the nonlinear convolutive mixture while in the present of quadratic nonlinearities. The proposed semi-blind model builds a cost function that offers an equalization solution by combining data, pilots, and the mutually referenced equalizer approach. The proposed method offers a number of benefits, including ease of implementation, resistance against channel order misspecification, and the ability to provide several equalization delays with a single solution. The simulation findings demonstrate that the proposed approach has fascinating performance characteristics and is resilient to low SNR.

Tensor decomposition has been demonstrated to be successful in a wide range of applications, from neuroscience and wireless communications to social networks. In an online setting, factorizing tensors derived from multidimensional data streams is however nontrivial due to several inherent problems of real-time stream processing. In recent years, many research efforts have been dedicated to developing online techniques for decomposing such tensors, resulting in significant advances in streaming tensor decomposition or tensor tracking. This topic is emerging and enriches the literature on tensor decomposition, particularly from the data stream analystics perspective. Thus, it is imperative to carry out an overview of tensor tracking to help researchers and practitioners understand its developments and achievements, summarise the current trends and advances, and identify challenging problems. In this article, we provide a contemporary and comprehensive survey on different types of tensor tracking techniques. We particularly categorize the state-of-the-art methods into three main groups: streaming CP decompositions, streaming Tucker decompositions, and streaming decompositions under other tensor formats (i.e., tensor-train, t-SVD, and BTD). In each group, we further divide the existing algorithms into sub-categories based on their main optimization framework and model architectures. Finally, we present several applications, research challenges, open problems, and potential directions of tensor tracking in the future.

We studied the problem of sparse subspace tracking in the high-dimensional regime where the dimension is comparable to or much larger than the sample size. Leveraging power iteration and thresholding methods, a new provable algorithm called OPIT was derived for tracking the sparse principal subspace of data streams over time. We also presented a theoretical result on its convergence to verify its consistency in high dimensions. Several experiments were carried out on both synthetic and real data to demonstrate the effectiveness of OPIT.

A novel class of fast Multi-Modulus algorithms (fastMMA) for Blind Source Separation (BSS) and deconvolution are presented in this work. These are obtained through a fast fixed-point optimization rule used to minimize the Multi-Modulus (MM) criterion. Here, two BSS versions are provided to separate the sources either by finding the separation matrix at once or by separating a single source each time using a fast deflation technique. Further, the latter method is extended to cover systems of convolutive nature. Interestingly, these algorithms are implicitly shown to belong to the fixed step-size gradient descent family, henceforth, an algebraic variable step-size is proposed to make these algorithms converge even much faster. Apart from being computationally and performance-wise attractive, the new algorithms are free of any user-defined parameters.

In this paper, we introduce a fast adaptive algorithm for CAN- DECOMP/PARAFAC decomposition of streaming three-way tensors using randomized sketching techniques. By leveraging randomized least-squares regression and approximating matrix multiplication, we propose an efficient first-order estimator to minimize an exponentially weighted recursive least- squares cost function. Our algorithm is fast, requiring a low computational complexity and memory storage. Experiments indicate that the proposed algorithm is capable of adaptive tensor decomposition with a competitive performance evaluation on both synthetic and real data.

In recent years, the prevalence of magnetic fields and their independence from extra infrastructure have attracted considerable interest in indoor localization based on magnetic field (MF) measurements. However, the low discrimination of MF measurements, the different MF measurements due to heterogeneous devices, and the interference with ferromagnetic materials are significant challenges for practical applications of indoor localization based on MF measurements. This paper first analyzes the statistical characteristics of MF measurements with the magnetometers embedded in smartphones and their calibration. It demonstrates that the MF measurements obey a Gaussian distribution and investigate the temporal stability and spatial distinguishability of MF-based measures. Secondly, it proposes removing magnetic field anomalies by the RLOWESS method and eliminating heterogeneous smartphone measurement differences using magnetometer calibration. Thirdly, it tests the localization performance of heterogeneous smartphones. The localization accuracy is 90% at 15m and drops to 60% when the test area increased to 45m. A high-quality embedded magnetometer had better localization performance than a low-quality magnetometer. Finally, it summarizes the feasibility/limitations of using only MF measurement for indoor localization.

In this paper, we introduce a novel approach for 2D blind multichannel identification using the helix transform in conjunction with the Cross Relation (CR) method. The helix transform is used to convert the 2D convolution of image and channels into 1D convolution. The CR method, known for its simplicity, efficiency and low computational cost, is then adapted and used to estimate the unknown channel coefficients. A main advantage of the proposed approach resides in its ability to help extending the plethora of methods from 1D to 2D blind system identification and ease their implementations.

This paper deals with semi-blind channel estimation in Single-Input Single-Output (SISO) Orthogonal Frequency Division Multiplexing (OFDM) communications system. The proposed algorithm proceeds in two main stages. The first one addresses the pilot-based Time-Of-Arrival (TOA) estimation using subspace methods and then estimates the channel through its specular model. In the second stage, one considers a decision feedback equalizer that is used to refine the channel parameters estimates. Simulation results show that good performance can be reached with only one OFDM pilot symbol with appropriate windowing using only one iteration. A significant performance improvement as compared to the pilot-based TOA method is observed.

This paper addresses the problem of adaptive blind sparse source separation in the time domain of an over-determined instantaneous noisy mixture. A two-step approach is proposed: first, the data are projected on the signal subspace estimated using the principal subspace tracker FAPI. In the second step, an ℓ1criterion is used to represent the sparsity property of the signal sources. For the optimization of this cost function, an adaptive method based on Givens and Shear rotations is used. This algorithm, referred to SGDS-FAPI, guarantees low computational complexity which is essential in the adaptive context. Numerical simulations have been performed, and showed that the proposed algorithm outperforms existing solutions in both convergence speed and estimation quality.

Dictionary learning algorithms have been successfully applied to a number of signal and image processing problems. In some applications however, the observed signals may have a multi-subpsace structure that enables block-sparse signal representations. Based on the observation that the observed signals can be approximated as a sum of low rank matrices, a new algorithm for learning a block-structured dictionary for block-sparse signal representations is proposed. It's derived via sequential penalized low rank matrix approximation, where a block coordinate descent approach is used to estimate the matrix pairs that form the different low rank matrix approximations. Experimental results on synthetic and standard gray-scale images illustrating the performance of the proposed algorithm are provided.

This study deals with semi‐blind (SB) channel estimation of multiple‐input multiple‐output orthogonal frequency‐division multiplexing (MIMO‐OFDM) system using maximum likelihood (ML) technique. For the ML cost optimisation function, new expectation maximisation (EM) algorithms for the channel taps estimation are introduced. Different approximation/simplification approaches are proposed for the algorithm's computational cost reduction. The first approach consists of decomposing the MIMO‐OFDM system into parallel multiple‐input single‐output OFDM systems. The EM algorithm is then applied to estimate the MIMO channel in a parallel way. The second approach takes advantage of the SB context to reduce the EM cost from exponential to linear complexity by reducing the size of the search space. Finally, the last proposed approach uses a parallel interference cancellation technique to decompose the MIMO‐OFDM system into several single‐input multiple‐output OFDM systems. The latter are identified in a parallel scheme and with a reduced complexity. The performance of the proposed approaches are discussed, assessed through numerical experiments and compared with respect to the Cramèr Rao Bound and to other EM‐based solutions reported in the literature.

Channel estimation, which is a key task in massive multiple‐input multiple‐output orthogonal‐frequency division‐multiplexing systems (MIMO‐OFDM), is severely affected by the problem of pilot contamination during the uplink transmission. Thus, the aim of this study is to investigate, via the Cramér‐Rao Bound tool, the effectiveness of semi‐blind (SB) methods for pilot contamination mitigation. For synchronous cells, these analyses demonstrate the possibility to efficiently solve the pilot contamination problem, with SB approaches, when considering a finite‐alphabet (non‐Gaussian) communications signal. However, considering only the signal's Second Order Statistics is not enough for solving such an issue even if the SB approach is adopted. Moreover, the analyses show that it is possible to get close to the optimal performance with a SB approach even if the pilots are non‐orthogonal as long as they are not fully coherent. For the asynchronous cells case, it has been demonstrated that the pilot contamination still occurs under small inter‐cell delays, but can be strongly mitigated with large inter‐cell delays.

In this study, the authors introduce new solutions and improvements to the multi‐channel blind image deconvolution problem. More precisely, authors’ contributions are threefold: (i) At first, a simplified version of the existing cross‐relation method for blind system identification is proposed; but most importantly, the authors incorporate into the channel estimation cost function a sparsity constraint to deal with the challenging issue of channel order overestimation errors; (ii) then, once the channel identification is achieved, a new image restoration method based on the stack decoding algorithm is introduced; and (iii) finally, a refining approach using an ‘all‐at‐once’ optimisation technique with an improved mixed norm regularisation is considered. The performance of the proposed approach was evaluated using several numerical simulations. Blind system identification and image restoration tasks were evaluated with respect to several criteria: numerical complexity, robustness to noise effects and channel order estimation. The results obtained are promising and highlight the effectiveness of the proposed approach.

This paper deals with semi-blind (SB) channel estimation of Multiple-Input Multiple-Output Orthogonal Frequency-Division Multiplexing (MIMO-OFDM) wireless communications system in the uplink transmission. Herein, we propose a new channel estimation approach using the well known EM technique. More precisely, we derive first the SB EM algorithm in the MIMO case. Then, a parallelizable version of this algorithm is introduced relying on the decomposition of the MIMO system into several MISO systems. Finally, we propose a reduced cost EM version where only the lattice points in the neighboring of the pilot-based detected symbols are considered.

This letter deals with a hybrid joint diagonalization problem considering both Hermitian and transpose congruence. Such problem can be encountered in certain noncircular signal analysis applications including blind source separation. We introduce new Jacobi-like algorithms using Givens or a combination of Givens and hyperbolic rotations. These algorithms are compared with state-of-the-art methods and their performance gain, especially in the high dimensional case, is assessed through simulation experiments including examples related to blind separation of noncircular sources.

In this paper, the problem of blind equalization and source separation of convolutive Multi-Input Multi-Output (MIMO) system is solved using Givens/Shear rotations. Targeting the Multi-Modulus (MM) signals and exploiting the second-order decorrelation among the transmitting sources, two efficient Givens Muti-Modulus (G-MMDDA) and Hyperbolic Givens (HG-MMDDA) Deconvolution algorithms are proposed for the first time. These solutions can be seen as extensions of the blind source separation MM-based method by Shah et al (2015) to the more general case of blind deconvolution for memory MIMO channels. The resulting algorithms are quite appealing as they combine both a good speed of convergence with low computational complexity.

This paper deals with semi-blind channel estimation Cramèr Rao Bound (CRB) performance of a multiuser Multiple-Input Multiple-Output Orthogonal Frequency-Division Multiplexing (MIMO-OFDM) wireless communication system in the uplink transmission. The first contribution shows that the Carrier Frequency Offset (CFO) impacts advantageously the CRB of the semi-blind channel estimation mainly due to the CFO cyclostationarity propriety. The second contribution states that when the relation between the subcarrier channel coefficients is not taken into account, i.e. without resorting to the inherent OFDM ‘channel structure’ during the channel estimation, results in a loss of the estimation performance. An evaluation of the significant performance loss resulting from this approach is provided.

This paper investigates the impact of certain side information that are available in the channels and/or signals on the blind system identification through the Cramer-Rao Bound (CRB). More precisely, we considered a Single Input Multiple Output (SIMO) system, and studied, for different scenarios, the performance bounds for channel estimation in both deterministic and Bayesian cases. The latter correspond to the situations where side information is brought by either a pilot sequence (semi-blind case), channel sparsity (specular channel case) or certain signal's statistical properties such as the non-circularity. This analysis allows us to have a better understanding of the behavior of the blind channel estimation when the considered side information is taken into account.

In this work, a novel subspace-based method for blind identification of multichannel finite impulse response systems is presented. Here, we exploit directly the block Toeplitz channel's structure in the signal's linear model to build a quadratic cost function, whose minimization leads to the desired channel estimation up to a scalar factor. This method can be extended to estimate any predefined linear structure, e.g., Hankel, that is usually encountered in linear systems. Simulation findings are provided to highlight the appealing advantages of the new structure-based subspace method over the standard subspace method in certain adverse identification scenarios.

Most communications systems require channel estimation for equalization and symbol detection. Currently, this is achieved by using dedicated pilot symbols, which consume a non-negligible part of the throughput and power resources, especially for large dimensional systems. The main objective of this paper is to quantify the rate of reduction of this overhead due to the use of a semi-blind channel estimation. Different data models and different pilot design schemes have been considered in this paper. By using the Cramér Rao Bound (CRB) tool, the estimation error variance bounds of the pilot-based and semi-blind based channel estimators for a multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) system are compared. In particular, for large MIMO-OFDM systems, a direct computation of the CRB is prohibitive, and hence, a dedicated numerical technique for its fast computation has been developed. Many key observations have been made from this comparative study. The most important one is that, thanks to the semi-blind approach, one can skip about 95% of the pilot samples without affecting the channel estimation quality.

We present a fast adaptive PARAFAC decomposition algorithm with low computational complexity. The proposed algorithm generalizes the Orthonormal Projection Approximation Subspace Tracking (OPAST) approach for tracking a class of third-order tensors which have one dimension growing with time. It has linear complexity, good convergence rate and good estimation accuracy. To deal with large-scale problems, a parallel implementation can be applied to reduce both computational complexity and storage. We illustrate the effectiveness of our algorithm in comparison with the state-of-the-art algorithms through simulation experiments.

This paper introduces a new PARAFAC algorithm for a class of third-order tensors. Particularly, the proposed algorithm is based on subspace estimation and solving a non-symmetrical joint diagonalization problem. To deal with large scale problem, a procedure for overcoming scale and permutation ambiguities is proposed in a parallel computing scheme leading to a significant cost reduction of our method. Performance comparison with some state-of-the-art algorithms produces promising results.

The issue of blind Multiple-Input and Multiple-Output (MIMO) deconvolution of communication system is addressed. Two new iterative Blind Source Separation (BSS) algorithms are presented, based on the minimization of Multi-Modulus (MM) criterion. A pre-whitening filter is utilized to transform the problem into finding a unitary beamformer matrix. Then, applying iterative Givens and Hyperbolic rotations results in Givens Multi-modulus Algorithm (G-MMA) and Hyperbolic G-MMA (HG-MMA), respectively. Proposed algorithms are compared with several BSS algorithms in terms of Signal to Interference and Noise Ratio (SINR) and Symbol Error Rate (SER) and it was shown to outperform them.

This letter proposes a novel technique for the blind separation of autoregressive (AR) sources. The latter relies on the joint diagonalization (JD) of appropriate AR matrix coefficients of the observed signals and can be applied to the separation of statistically dependent sources. The developed algorithm is referred to as `DARSS-JD' (for Dependent AR Source Separation using JD). Through the simulation experiments, DARSS-JD is shown to overcome existing second order separation methods with a relatively moderate computational cost.

Because of the denser active use of the spectrum, and because of higher radio telescope sensitivities, radio frequency interference (RFI) mitigation has become a sensitive topic for current and future radio telescope designs. In this paper, we consider different interference mitigation options which take advantage of both time-frequency and spatial RFI signatures. After specific subspace decompositions, these RFI spatial signatures are estimated and applied to pre- or post-correlation data by means of spatial filtering techniques based on projectors. We provide some performance analysis through simulations and Cramer-Rao Lower Bound derivations. In addition, recent results on real data from the LOFAR and EMBRACE radio telescopes are presented.

In this paper, we address the problem of delay detection in mobile localization context. A new methodology for delay detection is introduced, namely the Cell-Averaging Minimum Error Rate CA-MER detector, based on the minimization of the true error probability instead of minimizing only the miss probability for a constant false alarm rate. Simulation results show that the CA-MER detector operates better than the classical ones especially for low SNR values.

Statistical Resolution Limit (SRL), defined as the minimal separation to resolve two closely spaced signals, is one of the important tools to evaluate a given system performance. Based on S.T. Smith's formulation of the SRL, this paper provides a methodology to compute an approximate analytical expression of the resolution limit in the Gaussian model case. As an application, we consider the particular case of two sources located in the near field and consider the resolution limit in terms of minimum angular separation. Discussion and numerical illustrations are then given to get more insights on the proposed derivation and to validate our theoretical results.

Knowledge of the positions of sensor nodes is crucial for numerous applications in wireless sensors network. In this paper, we propose to use the Divided Difference Kalman Filter (DDKF) as a solution for locating and tracking a mobile node. This approach is an alternative variant of the nonlinear Kalman filtering, already used in this type of applications. The advantage of this approach is that it does not require calculation of the Jacobian as for the Extended Kalman Filter (EKF) and it does not need to use several parameters, as for the Unscented Kalman Filter (UKF) whose accuracy is closely dependent on the good choice of such parameters. In this work, a comparative performance study of four localization methods is conducted, namely the DDKF, the EKF, the UKF and the Least Squares Kalman Filter (LS-KF), which is a method based on multilateration in the least squares sense, followed by a smoothing step, using Kalman filtering. This study reveals many advantages in favor of the DDKF which, when applied for indoor localization, provides up to 40% gain in terms of Root Mean Squares Errors (RMSE) in position estimation, as compared to the other considered methods and which has a location error that is less than 2 meters in 95% of the considered cases.

ABSTRACT In this paper, we present a unified algorithm for phase and magnitude speech spectra data hiding. The phase and the magnitude speech spectra are concurrently investigated to increase the capacity and the security of the embedded information. The proposed algorithm in this paper is based on finding secure spectral embedding areas in wideband magnitude speech spectrum. Our approach exploits these areas to hide data in both speech components (i.e., phase and magnitude). The embedding locations and hiding capacity are defined according to a controlled acceptable distortion in the magnitude spectrum. The latter is expressed as a set of parameters controlled by the sender. Consequently, the hiding capacity and the locations of concealed data change for each data communication instance to further prevent malicious intrusions. Objective results show that the presented algorithm in this paper secures hidden data and achieves interesting tradeoffs between the hiding capacity and the speech quality. Copyright © 2013 John Wiley & Sons, Ltd.

This paper introduces new minor (noise) subspace tracking (MST) algorithms based on the minimum noise subspace (MNS) technique. The latter has been introduced as a computationally efficient subspace method for blind system identification. We exploit here the principle of the MNS, to derive the most efficient algorithms for MST. The proposed method joins the advantages of low complexity and fast convergence rate. Moreover, this method is highly parallelizable and hence its computational cost can be easily reduced to a very low level when parallel architectures are available. Different implementations are proposed for different contexts and they are compared via numerical simulations.

Beaconless position-based forwarding protocols have recently evolved as a promising solution for packet forwarding in wireless sensor networks. Quite a few variants of this class of forwarding protocols have been proposed over the years. One common observation is that they have all been evaluated from the perspective of a single node. Although useful, but a solid understanding of the end-to-end performance is still necessary. In this paper, we shed light on the end-to-end performance of beaconless position-based protocols along three distinct dimensions: energy, latency, and back-off probability. The latter is used as a direct indicator of the network's transport capacity. Consequently, we are able to provide an elaborate response to the question: what does it really cost to deliver a packet in a wireless sensor network using position-based beaconless forwarding protocols? In responding to this question, we highlighted the different performance tradeoffs inherent to beaconless position-based protocols. Furthermore, some operational recommendations are also provided.

Beaconless position-based forwarding protocols have recently evolved as a promising solution for packet forwarding in wireless sensor networks. However, as the node density grows, the overhead incurred in the process of relay selection grows significantly. As such, end-to-end performance in terms of energy and latency is adversely impacted. With the motivation of developing a packet forwarding mechanism that is tolerant to variation in node density, an alternative position-based protocol is proposed in this paper. In contrast to existing beaconless protocols, the proposed protocol is designed such that it eliminates the need for potential relays to undergo a relay selection process. Rather, any eligible relay may decide to forward the packet ahead, thus significantly reducing the underlying overhead. The operation of the proposed protocol is empowered by exploiting favorable features of orthogonal frequency division multiplexing (OFDM) at the physical layer. The end-to-end performance of the proposed protocol is evaluated against existing beaconless position-based protocols analytically and as well by means of simulations. The proposed protocol is demonstrated in this paper to be more efficient. In particular, it is shown that for the same amount of energy the proposed protocol transports one bit from source to destination much quicker.

Beaconless position-based forwarding protocols have recently evolved as a promising solution for packet forwarding in wireless sensor networks. However, as the network density grows, the overhead incurred grows significantly. As such, end-to-end energy and delay performance is adversely impacted. Motivated by the need for a forwarding mechanism that is more tolerant to growth in node density, an alternative position-based protocol is proposed in this paper. The protocol is designed such that it completely eliminates the need for potential relays to undergo a relay election process. Rather, any eligible relay may decide to forward the packet ahead, thus significantly reducing the overhead. The operation of the proposed protocol is empowered by exploiting favorable features of orthogonal frequency division multiplexing (OFDM) at the physical layer. End-to-end performance is evaluated here against existing beaconless protocols. It is demonstrated that the proposed protocol is more efficient since it is able to offer lower end-to-end delay for the same amount of energy consumption.

We present a two stage blind source separation (BSS) algorithm for robot audition. The algorithm is based on a beamforming preprocessing and a BSS algorithm using a sparsity separation criterion. Before the BSS step, we filter the sensors outputs by beamforming filters to reduce the reverberation and the environmental noise. As we are in a robot audition context, the manifold of the sensor array in this case is hard to model, so we use pre-measured Head Related Transfer Functions (HRTFs) to estimate the beamforming filters. In this article, we show the good performance of this method as compared to a single stage BSS only method.

In this paper, the quasi-convexity of a sum of quadratic fractions in the form Σi=1n[(1+ci x2)/((1+dix)2)] is demonstrated wherecianddiare strictly positive scalars, when defined on the positive real axis R+. It will be shown that this quasi-convexity guarantees it has a unique local (and hence global) minimum. Indeed, this problem arises when considering the optimization of the weighting coefficient in regularized semi-blind channel identification problem, and more generally, is of interest in other contexts where we combine two different estimation criteria. Note that V. Buchoux have noticed by simulations that the considered function has no local minima except its unique global minimum but this is the first time this result, as well as the quasi-convexity of the function is proved theoretically.

In this paper, we propose a new iterative algorithm for the minimization of the constant modulus (CM) criterion. It is composed of two stages: Prewhitening followed by complex Givens rotations, whence the name Givens CMA (GCMA). Initially, prewhitening reduces the channel matrix into a unitary matrix. Subsequently, this unitary matrix is computed via complex Givens rotations by minimizing the CM criterion. The proposed algorithm provides similar or even better performance than the Analytical CMA (ACMA) algorithm at reduced computational cost. Some simulation results are provided to illustrate the effectiveness of the proposed algorithm.

Based on the CRB of the 2D-DOA estimation problem, we prove a condition on the sensor coordinates of a planar array to be ambiguity-free and isotropic. A systematic search of such antenna arrays is conducted leading to the identification of all possible ambiguity-free isotropic arrays. In particular, we select the arrays that outperform the popular uniform circular array (UCA). It is shown that these arrays allow to enhance the DOA estimation by as much as 25%, in comparison with UCA. As the number of sensors increases, the best isotropic array tends towards the non-intuitive V-shape.

This paper addresses the issue of the optimization of the regularization constant in semi-blind channel estimation techniques, in which the training sequence-based criterion is combined linearly with the blind subspace criterion. In such semi-blind estimation techniques, the optimization of the regularizing constant with respect to the channel estimation error is mandatory, otherwise, the expected improvement in performance could not be achieved. In this context, recent works proposed numerical methods for the setting of the regularization constant. However, these methods are often sub-optimum and involve high computational complexities. In this paper, we propose to optimize with respect to a regularizing matrix instead of a regularizing scalar. We prove that interestingly in this case, a closed-form expression for the optimum regularizing matrix exists, thereby avoiding iterative algorithms as for the conventional techniques. We also prove that the obtained scheme has slightly better performance in terms of mean square error and bit error rate while ensuring lower complexity.

In OFDM systems, the inter-symbol interference and interblock interference are eliminated by using a Fourier transform and Cyclic Prefix (CP) redundancy of length higher than the channel size. To preserve a high bit rate, channel shortening technique is required to reduce the redundancy. Recently, a new channel shortening technique for differential encoded SISO-OFDM systems was introduced by Ma el al. This technique is very simple and achieves the channel shortening using only one symbol (the first emitted symbol). In this article, we propose a non-trivial extension of this method to MIMO-OFDM systems. More precisely, we exploit the structure of the first emitted symbol when using single block differential encoding to shorten MIMO channel. The simulation results demonstrate the effectiveness of this new shortening method.

This paper introduces new blind time domain equalization (TEQ) techniques for downlink multicarrier code division multiple access (MC-CDMA) systems. This equalization exploits the guard interval (GI) redundancy together with the excess codes (EC) to restore the structural properties of MC-CDMA signal destroyed by multipath fading channels. Indeed, since in CDMA based wireless systems, the number of users per cell is significantly less than the spreading factor, a base station can set apart a subset of the codes, the excess codes that will not be used for spreading. It is shown that the joint use of the EC and GI redundancy improves significantly the equalization performance compared to the GI only or EC only cases. A second contribution consists in exploiting the previous information together with the one given by a pilot sequence in a semi blind scheme to further improve the performance. Finally, we propose a new adaptive algorithm for the estimation and tracking of the equalizer that has a faster convergence rate compared to existing algorithms of similar complexity order. Simulation based performance evaluations are given at the end of this paper to assess the effectiveness of the proposed methods in MC-CDMA.

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 estimate 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.

This paper considers the problem of blind channel shortening in OFDM systems. Standard OFDM systems use guard interval (QI) in form of cyclic prefix (CP) and nulltones (NT) redundancy. In this paper, we are interested in exploiting simultaneously the CP and the NT to achieve blindly the channel shortening. We start by proving that the restoration of NT property leads to the desired channel shortening. However, the performance of the shortening based only on the NT is relatively poor. We propose to improve it by using the NT in conjunction with the CP redundancy. Hence, the Gl-based shortening criterion is combined with the NT-based criterion via a scalar weighting coefficient. The latter is optimized to improve symbol error rate (SER) performance of the receiver. Simulation results are provided to illustrate the performance of the combined criterion as compared to the GI-Based (MERRY algorithm) or NT-based criteria.

Subspace-based channel impulse response (CIR) estimators have been proposed for multi carrier code division multiple access (MC-CDMA) systems. Channel identifiability conditions have been formulated for blind (non-data-aided) CIR estimators. These identifiability conditions require the channel filtering matrix to satisfy certain rank conditions. In this paper, the generalized uplink MC-CDMA system model is analyzed in detail. It will be shown that each user's signal subspace dimensionality is independent of the user's spreading code and CIR. Furthermore, for any realization of the users' CIR, the channel identifiability is established by only imposing a simple rank condition on the users' spreading codes.

In this letter, we propose a new adaptive algorithm for the generalized symmetric eigenvalue problem, which can extract the principal and minor generalized eigenvectors, as well as their corresponding subspaces, at a low computational cost. A comparison with other adaptive algorithms from the literature, including the batch generalized singular value decomposition (GSVD) technique, is also given to show the superiority of the proposed algorithm in terms of convergence performance and computational complexity.

In this letter, we elaborate a new version of the orthogonal projection approximation and subspace tracking (OPAST) for the extraction and tracking of the principal eigenvectors of a positive Hermitian covariance matrix. The proposed algorithm referred to as principal component OPAST (PC-OPAST) estimates the principal eigenvectors (not only a random basis of the principal subspace as for OPAST) of the considered covariance matrix. Also, it guarantees the orthogonality of the weight matrix at each iteration and requires flops per iteration, where is the size of the observation vector and is the number of eigenvectors to estimate. (The number of flops per iteration represents the total number of multiplication, division, and square root operations that are required to extract the desired eigenvectors at each iteration.) The estimation accuracy and tracking properties of PC-OPAST are illustrated through simulation results and compared with the well-known singular value decomposition (SVD) method and other recently proposed PCA algorithms.

This paper presents a new mobile station (MS) localization method using round trip time (RTT) measurements in the UMTS-FDD [1]. Three or more base stations (BS) performing RTT measurements of a signal from a mobile station (MS) are necessary for its localization. However, when some of the measurements are from non-line-of-sight (NLoS) paths, the location errors can be very large. We propose a method that takes into account possible large RTT error measurements caused by NLoS. To this end, the new method measures the best coherence between the RTT estimates and allows the mobile to select the three most reliable measures. This method does not depend on a particular distribution of the NLoS error and allows the selection of the least dasianoisypsila RTT when all measurements are of LoS type. Theoretical analysis is provided to assess the performance gain of our proposed algorithm and validated by realistic simulation results.

This paper considers the blind separation of nonstationary sources in the underdetermined convolutive mixture case. We introduce two methods based on the sparsity assumption of the sources in the time-frequency (TF) domain. The first one assumes that the sources are disjoint in the TF domain; i.e. there is at most one source signal present at a given point in the TF domain. In the second method, we relax this assumption by allowing the sources to be TF-nondisjoint to a certain extent. In particular, the number of sources present (active) at a TF point should be strictly less than the number of sensors. In that case, the separation can be achieved thanks to subspace projection which allows us to identify the active sources and to estimate their corresponding time-frequency distribution (TFD) values.

In this paper, we present a theoretical stability analysis of the YAST algorithm used for tracking the noise subspace of the covariance matrix associated with time series. This analysis demonstrates the instability of the YAST and a more stable alternative solution is proposed. In addition to its stability, the resulting algorithm is less expensive than the YAST and has a computational complexity of order O(nr) flops per iteration where n is the size of the observation vector and r <; n is the minor subspace dimension. Finally, we pay a special attention to the case r = 1 due to its importance in many quadratic optimization problems. In that particular case, we propose a simplified version of the algorithm to estimate either the first principal eigenvector or the last minor eigenvector of the covariance matrix. Simulation results are provided at the end to validate the theoretical stability analysis and to illustrate the tracking capacity of the proposed algorithm.

Recently, certain solutions have been proposed to solve the common poles estimation problem in a multichannel exponentially damped sinusoidal (EDS) signal. In this work, we tackle the closely related problem consisting of the estimation of the complex amplitudes associated to the common poles of a multichannel EDS signal. Our approach is based on the estimation of an oblique projector on the space of the estimated/common poles along the space of the unknown/non-common poles. We derive three projection/estimation schemes. The first one is based on a sequential (channel by channel) processing of the data while the others are based on joint estimation of the complex amplitudes.

In this contribution, a new mobile station (MS) localization method is provided using round trip time (RTT) measurements in the UMTS-FDD. Three or more base stations (BS) performing RTT measurements of a signal from a mobile station (MS) are necessary for its localization. However, when some of the measurements are from non-line-of-sight (NLoS) paths, the location errors can be very large. We propose a method that takes into account possible large RTT error measurements caused by NLoS. To this end, the new method measures the best coherence between the RTT estimates and allows the mobile to select the three most reliable measures among the whole available RTT measurements. This method does not depend on a particular distribution of the NLoS error. Realistic simulations show the gain of positioning accuracy provided by the proposed algorithm.

This paper focuses on the blind image separation using their sparse representation in an appropriate transform domain. A new separation method is proposed that proceeds in two steps: (i) an image pre-treatment step to transform the original sources into sparse images and to reduce the mixture matrix to an orthogonal transform (ii) and a separation step that exploits the transformed image sparsity via an lscrp-norm based contrast function. A simple and efficient natural gradient technique is used for the optimization of the contrast function. The resulting algorithm is shown to outperform existing techniques in terms of separation quality and computational cost.

This paper considers the blind separation of nonstationary sources in the underdetermined convolutive mixture case. We introduce, two methods based on the sparsity assumption of the sources in the time-frequency (TF) domain. The first one assumes that the sources are disjoint in the TF domain, i.e., there is at most one source signal present at a given point in the TF domain. In the second method, we relax this assumption by allowing the sources to be TF-nondisjoint to a certain extent. In particular, the number of sources present (active) at a TF point should be strictly less than the number of sensors. In that case, the separation can be achieved thanks to subspace projection which allows us to identify the active sources and to estimate their corresponding time-frequency distribution (TFD) values. Another contribution of this paper is a new estimation procedure for the mixing channel in the underdetermined case. Finally, numerical performance evaluations and comparisons of the proposed methods are provided highlighting their effectiveness.

In this paper, we address the problem of separating N unknown sources using as many observed mixtures. The sources considered here are assumed to be of a non-stationary nature, i.e., their spectral contents are assumed to be time-varying. Using linear time-frequency (TF) representations of the mixtures along with a classification procedure based on vector clustering yield an effective way to separate the sources. Compared to other existing TF based separation methods, the proposed one is characterized by its simplicity and ease of implementation. Moreover, it can be applied in situations where others cannot. Specifically, the algorithm can handle monocomponent as well as multicomponent sources and its assumptions about the mixing matrix are more relaxed than other existing algorithms. Example is presented to prove the validity and efficiency of the proposed algorithms

We consider the problem of tracking the minor or principal subspace of a positive Hermitian covariance matrix. We first propose a fast and numerically robust implementation of Oja algorithm (FOOja: fast orthogonal Oja). The latter is said fast in the sense that its computational cost is of order O(np) flops per iteration where n is the size of the observation vector and p < n is the number of minor or principal eigenvectors we need to estimate. FOOja guarantees the orthogonality of the weight matrix at each iteration. Moreover, this algorithm is analyzed and compared with two other fast algorithms (OOjaH and FDPM) with respect to their numerical stability. Simulation results highlights the relatively good stability behavior of FOOja

In this paper, we propose a new blind minimum mean square error (MMSE) equalization algorithm of noisy single-input multiple-outputs finite impulse response (SIMO-FIR) systems, relying only on second order statistics. This algorithm offers an important advantage, a total independence of the channel order. Exploiting the fact that the equalizer filter belongs both, to the signal subspace and to the kernel of truncated data covariance matrix, the algorithm achieves blindly a direct estimation of the zero-delay MMSE equalizer parameters. The proposed approach has several features that are studied in this work. More precisely, we develop a two-step procedure to further improve the performance gain and to control the equalization delay. We present an efficient adaptive implementation of our equalizer, which reduces the computational complexity from O(n3) to O(n2p), where n is the data vector length and n is the number of sensors. Simulation results are provided to illustrate the effectiveness of the proposed blind equalization algorithm

In this paper we address a denoising technique based on calculation of non local means through neighborhoods. Non local neighborhoods are computed in a transformed domain, namely the wavelet domain. A noisy image is transformed using a lifting scheme. The wavelet coefficients in each subband image are modelized by a generalized Gaussian distribution (GGD) whose parameters (scale and shape parameters) are estimated using an appropriate technique. The estimated parameters are used to define a generalized non local mean which allows us to restore the original image. Processing in the wavelet domain is suitable since image are often available in a compressed domain, beside, processing smaller images allows us to reduce the computational cost

In this paper, we address the problem of separating unknown multi-component signals from their instantaneous mixtures. Using linear time-frequency (TF) representation of the mixtures along with vectors classification scheme provide us a simple and efficient technique to separate multicomponent signals. The proposed algorithm can handle monocomponent as well as multicomponent sources and its assumptions about the mixing matrix are more relaxed compared to other existing TF based algorithms. The source separation results for the mixed synthetic signals as well as mixed real audio signals, such as mixture of speech and music, are shown to illustrate the validity and efficiency of the proposed scheme

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 algorithm is said fast in the sense that its computational cost is of order O(np) flops per iteration where n is the size of the observation vector and p < n is the number of minor eigenvectors we need to estimate. This algorithm is based on a stochastic gradient technique and a fast orthogonalization procedure that guarantees the algorithm stability and the orthogonality of the weight matrix at each iteration. Despite its low computational cost, the proposed algorithm is quite efficient as shown by simulation experiments and performs better than other existing methods of higher computational complexity

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.

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.

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.

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.

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

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).

We investigate the performance of pilot-aided channel estimation and data detection for multi-input multi-output (MIMO) systems. We analyze and compare the Cramer-Rao bound (CRB) on channel tap estimation error for different design models of the pilot sequences. Then, the minimum mean square error (MMSE) estimation method with deflation is performed for channel identification for the embedded pilot scheme.

In this contribution, a mobile location method is introduced using angle of arrival's (AOAs) in UMTS-FDD systems with measurements from at least two different base-stations. Although computationally efficient, this method enhances state of the art algorithms based on a simple trilateration and takes into account error measurements caused by non-line-of-sight (NLOS) and near-far effect. The new method attributes an index of confidence for each measure, in order to allow the mobile to select the two most reliable measures and not to use all measures, equally.

We propose a blind separation technique for nonstationary sources that exploits both auto-terms and cross-terms of the time-frequency distributions. The technique is based on the simultaneous joint diagonalization and off-diagonalization of spatial time-frequency distributions. Computer simulations demonstrate the superiority of the approach in comparison with other time-frequency based methods.

In this work, we present an evolution of the Damped and Delayed Sinusoidal (DDS) model introduced within the framework of the general signal modeling. This model, named the Partial Damped and Delayed Sinusoidal (PDDS) model, takes into account a single time delay parameter for a set (sum) of damped sinusoids. The proposed modification is more consistent with the transient audio modeling problem. The validity of the approach is shown by its comparison with the well-known Exponentially Damped Sinusoids (EDS) approach. Finally, the performances of the three models based high-resolution parameter estimation algorithms are compared on synthetic fast time-varying signals, and on two typical audio transients.

This paper introduces an orthonormal version of the sliding-window projection approximation subspace tracker (PAST). The new algorithm guarantees the orthonormality of the signal subspace basis at each iteration. Moreover, it has the same complexity as the original PAST algorithm, and like the more computationally demanding natural power (NP) method, it satisfies a global convergence property, and reaches an excellent tracking performance.

We present an iterative Fourier-type algorithm which is introduced for DDS (damped and delayed sinusoid) parameter estimation using a sub-band processing approach. This algorithm is shown to improve on existing ones with respect to computational cost and estimation accuracy. Moreover, we derive the Cramer-Rao bound (CRB) expression for the DDS model and we perform a simulation-based performance analysis of a noisy fast time-varying synthetic signal and in the audio transient signal modeling context.

The problem of blind system identification (BSI) of a single-input multiple-output (SIMO) finite impulse response (FIR) channel is addressed. A plethora of methods and techniques for BSI have been proposed in the literature so far, including the subspace method. A difficulty with the subspace based identification methods is their sensitivity to misspecification of the channel order. We propose an identification algorithm robust to channel order overestimation. This algorithm is based on the minimization of a constant modulus (CM) in conjunction with subspace orthogonality criteria. Numerical simulations and investigations are presented to demonstrate the potential of the proposed algorithms.

We address the problem of image restoration in a multichannel system degraded by unknown blurs and additive noise. To identify the unknown blurs, modelled as FIR channels, we propose a subspace based method which exploits an orthogonality property between signal and noise subspaces. Finally, to demonstrate the performance of the proposed method, we run simulations and compare the results with those of the cross relation method.

We explore code-division multiple-access systems with multiple transmitter and receiver antennas combined with algebraic constellations over a quasi-static multipath fading channel. We first propose a technique to obtain transmit diversity for a single user over quasi-static fading channels by combining algebraic constellations with full spatial diversity and spreading sequences with good cross-correlation properties. The proposed scheme is then generalized to a multiuser system using the same algebraic constellation and different spreading sequences. We also propose a linear multiuser detector based on the combination of linear decorrelation with respect to all users, and the application of the sphere decoder to decode each user separately. Finally, we consider the generalization to multipath fading channels where the additional diversity advantage due to multipath is exploited by the sphere decoder, and a method of blind channel estimation based on subspace decomposition is examined.

In this paper, we propose a novel algorithm to select and extract separately all the components, using the time-frequency distribution (TFD), of a given multicomponent frequency-modulated (FM) signal. This algorithm does not use any a priori information about the-various components. However, its performance highly depends on the cross-terms suppression ability and high time-frequency resolution of the considered TFD. The algorithm is compared with the higher-order ambiguity function (HAF) algorithm for the estimation of the phase-coefficients of a multicomponent signal. Monte-Carlo simulations results show the superiority of the proposed algorithm.

In this paper, we present a novel parametric model based on an important evolution of McAulay & Quatieri sinusoidal models [5]. This model takes into account a damping factor as well as a delay parameter which makes it more efficient for modeling strong audio transients such as castanets onsets without pre-echo effects'. We also develop an original algorithm to estimate the model parameters by means of a high resolution method followed by a sub-band analysis.

This paper deals with the near field source localization problem using the array output second order statistics (SOS). 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.

We construct a new family of linear space-time (ST) block codes by the combination of rotated constellations and the Hadamard transform, and we prove them to achieve the full transmit diversity over a quasi-static or fast fading channels. The proposed codes transmit at a normalized rate of 1 symbol/s. When the number of transmit antennas n=1, 2, or n is a multiple of four, we spread a rotated version of the information symbol vector by the Hadamard transform and send it over n transmit antennas and n time periods; for other values of n, we construct the codes by sending the components of a rotated version of the information symbol vector over the diagonal of an n /spl times/ n ST code matrix. The codes maintain their rate, diversity, and coding gains for all real and complex constellations carved from the complex integers ring Z [i], and they outperform the codes from orthogonal design when using complex constellations for n > 2. The maximum-likelihood (ML) decoding of the proposed codes can be implemented by the sphere decoder at a moderate complexity. It is shown that using the proposed codes in a multiantenna system yields good performances with high spectral efficiency and moderate decoding complexity.

A new signal processing method is developed for solving the multiline fitting problem in a two dimensional image. We first reformulate the former problem in a special parameter estimation framework such that a first order or a second order polynomial phase signal structure is obtained. Then, the previously developed algorithms in that formalism (and particularly the downsampling technique for high resolution frequency estimation) can be exploited to produce accurate estimates for line parameters. This method is able to estimate the parameters of parallel lines with different offsets and handles the quantization noise effect which can not be done by the sensor array processing technique introduced by Aghajan et al. (1993). Simulation results are presented to demonstrate the usefulness of the proposed method.

We address the problem of blind source separation of non-stationary signals of which only instantaneous linear mixtures are observed. A blind source separation approach exploiting both auto-terms and cross-terms of the time-frequency (TF) distributions of the sources is considered. The approach is based on the simultaneous diagonalization and anti-diagonalization of spatial TF distribution matrices made up of, respectively, auto-terms and cross-terms. Numerical simulations are provided to demonstrate the effectiveness of the proposed approach and compare its performances with existing TF-based methods.

We present two normalized versions of Oja's (1992) algorithm (NOja and NOOja), which can be used for the estimation of minor and principal subspaces of a vector sequence. The new algorithms offer, as compared to Oja, a faster convergence, orthogonality, and a better numerical stability with a slight increase in computational complexity.

This paper presents a generalized sufficient and necessary identifiability condition for second order statistics (SOS) based blind source separation (BSS). It is then shown that, even when this condition is not satisfied the sources are still partially identifiable (in blocks). Furthermore, although the identifiability condition depends on the autocorrelation coefficients of the unknown source signals, it can be tested directly from the observations. This issue is of prime importance to decide whether the sources have been well separated or else if further treatments are needed. Some simulation examples are given to assess our theoretical results.

We present a generalized sphere decoding (GSD) algorithm and its application for detecting information symbols in the case where one has a system with more inputs (symbols) than outputs or the opposite situation. We study the special case of a multi-antenna scenario in a cellular system with N antennas at the mobile and M/spl ges/N antennas at the base station. GSD reaches the maximum likelihood (ML) performance for both up and down link with a moderate complexity.

This paper presents a new approach for blind identification of multiple colored stationary/nonstationary signals distorted by unknown FIR channels. The key idea of this approach is to use a bank of decorrelators to transform a multiple-input and multiple output system (driven by the desired signals) into a bank of single-input and single-output systems. This approach is referred to as BID, i.e., blind identification via decorrelation. The BID approach can uniquely (up to a permutation and scaling) identify the signals if (a) the signals are mutually uncorrelated and of distinct power spectra, and (b) each column of the system function is a coprime polynomial vector. No other method known to date can uniquely identify the signals under the above condition. The BID approach achieves the optimal identifiability potential predicted by Hua and Tugnait.

The problem of frequency offset and channel estimation is essential for multiuser detectors in a DS-CDMA system. An efficient algorithm for joint estimation of these parameters without using training sequences is proposed. The approach exploits the ESPRIT algorithm by collecting two samples within each chip duration and constructing an approximate shift invariance structure. The high-resolution and noise-robust frequency offset estimation can therefore be achieved. Although the proposed algorithm is suboptimal relative to the maximum likelihood estimators, it provides the closed-form solutions and much less complexity in computation.

In this letter, we propose an orthogonalized version of the Oja algorithm (OOja) that can be used for the estimation of minor and principal subspaces of a vector sequence. The new algorithm offers, as compared to Oja, such advantages as orthogonality of the weight matrix, which is ensured at each iteration, numerical stability, and a quite similar computational complexity.

Subspace decomposition has proven to be an important tool in adaptive signal processing. A number of algorithms have been proposed for tracking the dominant subspace. Among the most robust and most efficient methods is the projection approximation and subspace tracking (PAST) method. This paper elaborates on an orthonormal version of the PAST algorithm for fast estimation and tracking of the principal subspace or/and principal components of a vector sequence. The orthonormal PAST (OPAST) algorithm guarantees the orthonormality of the weight matrix at each iteration. Moreover, it has a linear complexity like the PAST algorithm and a global convergence property like the natural power (NP) method.

This letter considers the separation and estimation of independent sources from their instantaneous linear mixed observed data. Here, unknown source signals are estimated from their unknown linear mixtures using the strong assumption that the sources are mutually independent. In practice, separation can be achieved by using suitable second- or higher-order statistics. The authors propose a novel source separation technique exploiting fourth-order time frequency distributions. A computationally feasible implementation is presented based on joint diagonalization of the matrices of the principal slices of time-multifrequency domain of support of the cumulant-based Wigner trispectrums. A numerical example demonstrates the effectiveness of the proposed approach.

We propose blind multiuser detection schemes with antenna arrays, which is based on signal subspace estimation. They are a multichannel extension of the decorrelating and minimum mean-square-error detectors, and therefore they share their immunity to near-far effects. The blind scheme may be seen as an extension of the results in of Wang and Poor (see IEEE Trans. Inform. Theory, vol.44, p.677-90, 1998). However, it is seen that compared with the latter results when spatial diversity is considered, the proposed spatio-temporal detectors offer, with little attendant increase in computational complexity, a better performance. A blind adaptive implementation based on a new orthogonal PAST (projection approximation subspace tracking) algorithm, which is shown to be efficient for subspace tracking, is proposed. Also, we develop a blind estimation of the spatial signature based on the orthogonality between noise and signal subspaces. It is seen that the blind adaptive multiuser detection and blind spatio-temporal signature estimation can he integrated jointly.

Separation of sources that are mixed by an unknown (hence, "blind") mixing matrix is an important task for a wide range of applications. This paper presents an adaptive blind source separation method using second order statistics (SOS) and natural gradient. The SOS of observed data is shown to be sufficient for separating mutually uncorrelated sources provided that the temporal coherences of all sources are linearly independent of each other. By applying the natural gradient, new adaptive algorithms are derived that have a number of attractive properties such as invariance of asymptotical performance (with respect to the mixing matrix) and guaranteed local stability. Simulations suggest that the new algorithms are highly efficient and outperform some of the best existing ones.

We address the problem of joint Schur decomposition (JSD) of several matrices. This problem is of great importance for many signal processing applications such as sonar, biomedicine, and mobile communications. We first present a least-squares (LS) approach for computing the JSD. The LS approach is shown to coincide with that proposed intuitively by Haardt et al. (1996), thus establishing the optimality of their criterion in the least-squares sense. Following the LS criterion, we then propose new Jacobi-like algorithms that extend and improve the existing JSD algorithms. An application of the new JSD algorithm to multidimensional harmonic retrieval is also presented.

Blind system identification (BSI) is a fundamental signal processing technology aimed at retrieving a system's unknown information from its output only. This technology has a wide range of possible applications such as mobile communications, speech reverberation cancellation, and blind image restoration. This paper reviews a number of recently developed concepts and techniques for BSI, which include the concept of blind system identifiability in a deterministic framework, the blind techniques of maximum likelihood and subspace for estimating the system's impulse response, and other techniques for direct estimation of the system input.

We present a new subspace-based method for blind identification of multichannel finite impulse response (FIR) systems. Instead of assuming spatially white additive noise as commonly used, we consider the case where the noise spatial covariance matrix is unknown. We show how a standard subspace method can be simply modified so that the channel estimate does not depend on the zero-lag correlation coefficient of the observation vector and, thus, is independent of the spatial covariance matrix of the additive noise.

Direction of arrival (DOA) estimation techniques require knowledge of the sensor-to-sensor correlation of the noise, which constitutes a significant drawback. In the case of temporally correlated signals, it is possible to estimate the signal parameters without any assumptions made on the spatial covariance matrix of the noise. A new method for the estimation of the signal subspace and noise subspace is introduced. The proposed approach is based on a joint block-diagonalization (JBD) of a combined set of spatio-temporal correlation matrices. Once the signal and the noise subspaces are estimated, any subspace based approach can be applied for DOA estimation. A performance comparison of the proposed approach with an existing technique is provided.

The problem of blind identification of p-inputs/q-outputs FIR transfer functions is addressed. Existing subspace identification methods derived for p=1 are first reformulated. In particular, the links between the noise subspace of a certain covariance matrix of the output signals (on which subspace methods build on) and certain rational subspaces associated with the transfer function to be identified are elucidated. Based on these relations, we study the behavior of the subspace method in the case where the order of the transfer function is overestimated. Next, an asymptotic performance analysis of this estimation method is carried out. Consistency and asymptotical normality of the estimates is established. A closed-form expression for the asymptotic covariance of the estimates is given. Numerical simulations and investigations are presented to demonstrate the potential of the subspace method. Finally, we take advantage of our new reformulation to discuss the extension of the subspace method to the case p>1. We show where the difficulties lie, and we briefly indicate how to solve the corresponding problems. The possible connections with classical approaches for MA model estimations are also outlined.

In this paper, we address the problem of the blind identification of linear-quadratic instantaneous mixture of statistically independent random variables. This problem consists in the identification of an unknown linear-quadratic transmission channel excited by temporally correlated and mutually independent source signals, using only statistical information on the observations received by an array of sensors. Herein we propose a new technique of blind identification of this non-linear mixture based on joint diagonalization of a set of data correlation matrices. Several numerical simulations are presented to demonstrate the effectiveness of the method in the case of a quadratic phase-coupling mixture.

Blind channel identification methods based on the oversampled channel output is a problem of theoretical and practical interest. It is first demonstrated that the subspace methods developed in Moulines are not robust to errors in the determination of the model order. An alternative solution is then proposed, based on a linear prediction approach. The effect of overestimating the channel order is investigated by simulations: it is demonstrated that the prediction error method is "robust" to over-determination.

The article deals with the problem of blind separation of an instantaneous linear mixture of mutually uncorrelated sources. A solution based on the joint diagonalisation of a set of whitened correlation matrices has been proposed, along with an efficient algorithm to solve it. A second order source separation technique exploiting the time coherence of the source signals is considered. Asymptotic performance analysis of the proposed method is performed. Several numerical simulations are presented to demonstrate the effectiveness of the proposed method and to validate the theoretical expression of the asymptotic performance index.>