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Channel order estimation is a critical step in most blind single-input multiple-output (SIMO) channel identification/equalization algorithms. Several methods for estimating either the true channel order or its most significant part (the so-called effective channel order) have been recently proposed, but a solution able to work in practical scenarios (low or moderate signal-to-noise ratios (SNR5) and channels with small leading and/or trailing coefficients) has not been found yet. In this paper, a new criterion for effective channel order detection of SIMO channels is presented. The method is based on the fact that the cost function typically used in blind identification algorithms decreases monotonically with the estimated channel order, whereas for blind equalization algorithms, the cost function increases monotonically. In this paper, it is shown that a straightforward combination of both cost functions attains its minimum at the correct channel order even for moderate SNRS. The proposed method is able to work with small data sets, colored signals, and channels with small head and tail taps, which is a common problem in communication applications. The improvement of the proposed criterion over a number of existing algorithms is demonstrated through simulations. [ABSTRACT FROM AUTHOR]

Dynamic system modeling plays a crucial role in the development of techniques for stationary and nonstationary signal processing. Due to the inherent physical characteristics of systems under investigation, nonnegativity is a desired constraint that can usually be imposed on the parameters to estimate. In this paper, we propose a general method for system identification under nonnegativity constraints. We derive the so-called nonnegative least-mean-square algorithm (NNLMS) based on stochastic gradient descent, and we analyze its convergence. Experiments are conducted to illustrate the performance of this approach and consistency with the analysis. [ABSTRACT FROM AUTHOR]

Multidimensional vector cross-product has not been studied from the perspective of signal processing so far to the best of our knowledge and hence no effort has been made to develop any generalized algorithm for its hardware implementation. In this paper firstly, we identify the application domain of cross-product in signal processing and then introduce a novel recursive algorithm for generalized nD cross-product computation considering three dimensional (3D) cross-product as the fundamental operation. Subsequently a generalized scheme for architecture implementation for nD cross-product is proposed based on this algorithm. Secondly, we show that if 4D cross-product is used as the fundamental operation for formulating nD cross-product problem, then exploiting the inherent mathematical symmetry of 4D cross-product it is possible to reduce the total hardware cost of nD cross-product significantly at the architecture level (symmetry-based approach). Mathematical models for hardware complexity and operational delay have been formulated both for the generalized and symmetry-based approaches. [ABSTRACT FROM AUTHOR]

Markovian jump systems (MJSs) evolve in a jump-wise manner by switching among simpler models, according to a finite Markov chain, whose parameters are commonly assumed known. This paper addresses the problem of state estimation of MJS with unknown transition probability matrix (TPM) of the embedded Markov chain governing the jumps. Under the assumption of a time-invariant but random TPM, an approximate recursion for the TPMs posterior probability density function (PDF) within the Bayesian framework is obtained. Based on this recursion, four algorithms for online minimum mean-square error (MMSE) estimation of the TPM are derived. The first algorithm (for the case of a two-state Markov chain) computes the MMSE estimate exactly, if the likelihood of the TPM is linear in the transition probabilities. Its computational load is, however, increasing with the data length. To limit the computational cost, three alternative algorithms are further developed based on different approximation techniques-truncation of high order moments, quasi-Bayesian approximation, and numerical integration, respectively. The proposed TPM estimation is naturally incorporable into a typical online Bayesian estimation scheme for M.JS [e.g., generalized pseudo-Bayesian (GPB) or interacting multiple model (IMM)]. Thus, adaptive versions of MJS state estimators with unknown TPM are provided. Simulation results of TPM-adaptive IMM algorithms for a system with failures and maneuvering target tracking are presented. [ABSTRACT FROM AUTHOR]

The objective of the beamforming with the exploitation of a sensor array is to enhance the signals of the sources from desired directions, suppress the noises and the interfering signals from other directions, and/or simultaneously provide the focalization of the associated sources. In this paper, we present a higher order cumulant-based beamforming algorithm, namely, the super-exponential blind adaptive beamforming algorithm, which is extended from the super-exponential algorithm (SEA) and the inverse filter criteria (IFC). While both SEA and IFC assume noise-free conditions, this requirement is no longer needed, and all the noise components are taken into account in the proposed algorithm. Two special conditions are derived under which the proposed blind beamforming algorithm achieves the performance of the corresponding optimal nonblind beamformer in the sense of minimum mean square error (MMSE). Simulation results show that the proposed algorithm is effective and robust to diverse initial weight vectors; its performance with the use of the fourth-order cumulants is close to that of the nonblind optimal MMSE beamformer. [ABSTRACT FROM AUTHOR]

A delay-dependent approach to robust H∞ filtering is proposed for linear discrete-time uncertain systems with multiple delays in the state. The uncertain parameters are supposed to reside in a polytope and the attention is focused on the design of robust filters guaranteeing a prescribed H∞ noise attenuation level. The proposed filter design methodology incorporates some recently appeared results, such as Moon's new version of the upper bound for the inner product of two vectors and de Oliveira's idea of parameter-dependent stability, which greatly reduce the overdesign introduced in the derivation process. In addition to the full-order filtering problem, the challenging reduced-order case is also addressed by using different linearization procedures. Both full- and reduced-order filters can be obtained from the solution of convex optimization problems in terms of linear matrix inequalities, which can be solved via efficient interior-point algorithms. Numerical examples have been presented to illustrate the feasibility and advantages of the proposed methodologies. [ABSTRACT FROM AUTHOR]

A fully decentralized sensor network, without fusion center, is deployed to estimate the position of a target. Taking advantage of the limited communication range of the nodes, and exploiting their (unknown) location inside the surveyed area, the likelihood profile is approximately reconstructed. A distributed ML-like estimator is, therefore, proposed and its asymptotic performance is investigated analytically, while computer experiments assess the behavior of the estimator in nonasymptotic regimes. The differences between one- and two-dimensional scenarios are also discussed. [ABSTRACT FROM AUTHOR]

A new blind subspace-based algorithm for signature waveform estimation in binary phase-shift-keying (BPSK)-modulated direct-sequence code-division multiple-access (DS-CDMA) systems is proposed. Our approach can be viewed as a modification of the popular blind technique by Liu and Xu (LX), which additionally exploits noncircularity of the transmitted signals and circularity of noise to increase the dimension of the observation space twice while keeping the dimension of the signal subspace unchanged. This enables us to improve the performance substantially and to apply the proposed algorithm to scenarios with an increased number of active users and lengthier user channels as compared with the original LX algorithm. The theoretical analysis of the identifiability of the proposed technique is provided, which shows that it enjoys more relaxed identifiability conditions than the LX algorithm. Analytical expressions for the mean-square error (MSE) of the proposed algorithm are also derived. Numerical examples verify the advantages of the proposed algorithm over the LX technique and validate our analytical results. [ABSTRACT FROM AUTHOR]

This paper presents a stochastic analysis of the delayed least-mean-Square (DLMS) adaptive algorithm with leakage. This analysis is obtained taking into account that mismatches between the system delay and its estimate may occur. Such an approach is not considered in previous models. In addition, it is shown that the introduction of a leakage factor into the adaptive algorithm keeps the adaptive algorithm stable under an imperfect delay estimate condition. Recursive difference equations for the first and second moments of the adaptive filter weights are derived. An expression for the critical value of the step size is also determined. Results of Monte Carlo simulations present excellent agreement with the proposed model for both white and colored Gaussian inputs. [ABSTRACT FROM AUTHOR]

Deals with a study that presented a technique for blindly starting up a minimum-mean-squared-error (MMSE) receiver for a user entering a code-division multiple-access system. Signal model; Details on the approach used in the blind startup of MMSE receivers; Direct startup algorithm for limited case; Simulations.

A novel framework of compressed sensing, namely statistical compressed sensing (SCS), that aims at efficiently sampling a collection of signals that follow a statistical distribution, and achieving accurate reconstruction on average, is introduced. SCS based on Gaussian models is investigated in depth. For signals that follow a single Gaussian model, with Gaussian or Bernoulli sensing matrices of \cal O(k) measurements, considerably smaller than the \cal O(k \log(N/k)) required by conventional CS based on sparse models, where N is the signal dimension, and with an optimal decoder implemented via linear filtering, significantly faster than the pursuit decoders applied in conventional CS, the error of SCS is shown tightly upper bounded by a constant times the best k-term approximation error, with overwhelming probability. The failure probability is also significantly smaller than that of conventional sparsity-oriented CS. Stronger yet simpler results further show that for any sensing matrix, the error of Gaussian SCS is upper bounded by a constant times the best k-term approximation with probability one, and the bound constant can be efficiently calculated. For Gaussian mixture models (GMMs), that assume multiple Gaussian distributions and that each signal follows one of them with an unknown index, a piecewise linear estimator is introduced to decode SCS. The accuracy of model selection, at the heart of the piecewise linear decoder, is analyzed in terms of the properties of the Gaussian distributions and the number of sensing measurements. A maximization-maximization (Max-Max) algorithm that iteratively estimates the Gaussian models parameters, the signals model selection, and decodes the signals, is presented for GMM-based SCS. In real image sensing applications, GMM-based SCS is shown to lead to improved results compared to conventional CS, at a considerably lower computational cost. [ABSTRACT FROM AUTHOR]

We address a p-shift finite impulse response (FIR) unbiased estimator (UE) for linear discrete time-varying filtering (p=0), p-step prediction (p>0), and p-lag smoothing (p<0) in state space with no requirements for initial conditions and zero mean noise. A solution is found in a batch form and represented in a computationally efficient iterative Kalman-like one. It is shown that the Kalman-like FIR UE is able to outperform the Kalman filter if the noise covariances and initial conditions are not known exactly, noise is not white, and both the system and measurement noise components need to be filtered out. Otherwise, the errors are similar. Extensive numerical studies of the FIR UE are provided in Gaussian and non-Gaussian environments with outliers and temporary uncertainties. [ABSTRACT FROM PUBLISHER]

We propose a sparsity-based approach to track multiple targets in a region of interest using an orthogonal-frequency-division multiplexing (OFDM) radar. We observe that in a particular pulse interval the targets lie at a few points on the delay-Doppler plane and hence we exploit that inherent sparsity to develop a tracking procedure. The use of an OFDM signal not only increases the frequency diversity of our system, as different scattering centers of a target resonate variably at different frequencies, but also decreases the block-coherence measure of the equivalent sparse measurement model. In the tracking filter, we exploit this block-sparsity property in developing a block version of the compressive sampling matching pursuit (CoSaMP) algorithm. We present numerical examples to show the performance of our sparsity-based tracking approach and compare it with a particle filter (PF) based tracking procedure. The sparsity-based tracking algorithm takes much less computational time and provides equivalent and sometimes better, tracking performance than the PF-based tracking. [ABSTRACT FROM AUTHOR]

This correspondence deals with time-varying (TV) single-input multiple-output (SIMO) channels, which are both frequency selective (due to high data rate) and time selective (due to mobility). A complex exponential basis expansion model (CE-BEM) is used to model the channel. We consider a block transmission system, where on the transmit side a precoder is employed to enable the maximum available diversity for a CE-BEM channel. After direct decoding on the receive side, the resulting channel resembles a finite-impulse-response (FIR) filter on both block and symbol level. We therefore propose an equalizer that bears a structure analogous to the effective channel. In comparison with a standard block minimum mean-square error decision-feedback equalizer (BMMSE-DFE) that involves the inversion of a large-size matrix, the proposed parametric equalizer renders a similar performance but at a lower computational cost if there are multiple outputs present. Another contribution of this correspondence is a semiblind algorithm to estimate this equalizer when the channel state information is not available: the equalizer taps and the information symbol estimates are refined recursively by means of normalized least-mean-squares (NLMS) adaptation. [ABSTRACT FROM AUTHOR]

An extension of the AutoRegressive (AR) model is studied, which allows transformations and distortions on the regressor to be handled. Many important signal processing problems are amenable to this Extended AR (i.e., EAR) model. It is shown that Bayesian identification and prediction of the EAR model can be performed recursively, in common with the AR model itself. The EAR model does, however, require that the transformation be known. When it is unknown, the associated transformation space is represented by a finite set of candidates. What follows is a Mixture-based EAR model, i.e., the MEAR model. An approximate identification algorithm for MEAR is developed, using a restricted Variational Bayes (VB) method. This restores the elegant recursive update of sufficient statistics. The MEAR model is applied to the robust identification of AR processes corrupted by outliers and bunt noise, respectively, and to click removal for speech. [ABSTRACT FROM AUTHOR]

A system composed of a single transmitter, M receiving antennas, and a single receiving channel is studied. Echo radar signals are acquired in a number of the cycles and used for estimation of the angle, velocity, and range in a multiple moving target scenario. The receiving antennas are switched to the single receiving channel periodically once every cycle. The developed algorithms are based on minimum variance (Capon) beamforming. A new nonstationary version of Capon beamforming is developed for the time-varying steering vector model. It is shown in simulations done for an automotive scenario that the proposed algorithms yield very good performance in terms of both estimation accuracy and multiple-target resolution. The Capon-based estimation gives results close to the corresponding Cramér-Rao lower bounds (CRLBs). [ABSTRACT FROM AUTHOR]

Focuses on a study which applied the time-frequency and time-scale analysis techniques to symbolic signals and describes a general linear approach to developing processing algorithms for symbolic signals. Description of an approach to the spectral and scale analysis of symbolic data; Correlation structure using frequency-domain or wavelet-domain analysis; Approaches for processing signals within mathematical domains.

Suggests a low-complexity algorithm for estimating both the direction of arrival (DOA) and the spread angle of spatially sources. Array signal processing; Parameter estimation; Information on Rayleigh channels.