Showing papers in "Signal Processing in 1996"

TL;DR: Intended for a one-semester advanced graduate course in digitalsignal processing or as a reference for practicing engineers and researchers, this book is no longer being mass-produced.

TL;DR: The problem of estimating the nominal direction of arrival and angular spread of a source surrounded by a large number of local scatterers using an array of sensors using an arrays of sensors is addressed and an optimally weighted least-squares criterion is proposed.

TL;DR: A sliding Goertzel algorithm to accurately estimate the Fourier coefficients of multifrequency (MF) sinusoidal signals buried in noise is presented, based on second-order digital resonators that are tuned at the desired frequencies.

TL;DR: It is shown that the MOESP (Multivariable Output-Error State sPace) class of subspace model identification schemes can be extended to identify Wiener systems, a series connection of a linear dynamic system followed by a static nonlinearity.

TL;DR: This decomposition may be seen as a formal extension of the eigenvalue decomposition (EVD), known for symmetric matrices, and links with the theory of homogeneous polynomials in several variables (i.e. quantics) are pointed out.

TL;DR: Kalman filtering methods are derived to track the channel by employing a multichannel autoregressive description of the time-varying taps in a decision-feedback equalization framework using higher-order statistics in order to estimate the model parameters from input/output data.

TL;DR: A general tool for multichannel and multipath problems is given in FIR matrix algebra using abstract algebra/group theoretic concepts, information theoretic principles, and the Bussgang property, methods of single channel filtering and source separation of multipath mixtures are merged into a general FIR matrix framework.

TL;DR: The present paper stresses how the basic idea is to focus on the estimation of the state-variable candidates — the k-step ahead output predictors — by recomputing these from a ‘non-parametric’ (or, rather, high order ARX) one- step ahead predictor model, closed loop data can be handled.

TL;DR: This work shows how the reproducing kernel may be used for defining and evaluating the scale and angle-resolving power of a wavelet, and illustrates the usefulness of the scale-angle representation of the CWT on the problem of disentangling a train of damped plane waves.

TL;DR: The objective of this paper is to establish insightful connections among these studies and present recent developments of blind channel equalization, and unify various representative algorithms into a common theoretical framework.

TL;DR: A new subspace identification algorithm for the identification of multi-input multi-output linear time-invariant continuous-time systems from measured frequency response data is presented and it is shown that, when the error distribution on the measurements is given, the algorithm can be made asymptotically unbiased.

TL;DR: Four subspace algorithms which are based on an initial estimate of the state are considered and an algorithm which is essentially equal to the N4SID algorithm by Van Overschee and De Moor is compared.

TL;DR: It is observed that the modified MUSIC works significantly better than the ordinary MUSIC at different SNR in terms of the mean squared error.

TL;DR: A new method for transmission beamforming is presented, which, by exploiting the subspace structure present in the spatial channel, requires much lower feedback data rates than previous methods.

TL;DR: This approach to estimating the number of pedestrians present in a real-life complex scene by processing a sequence of images improves the estimation accuracy obtained by using only current available data, and can predict crowding values between two successive acquisitions.

TL;DR: The conclusion is that 4SID can be viewed as a linear regression multistep-ahead prediction error method with certain rank constraints with certainRank constraints within the standard framework of system identification and linear regression estimation.

TL;DR: It turns out that, under weak conditions, both PAST and PASTd globally converge to the desired signal subspace or signal eigenvectors and eigenvalues with probability one.

TL;DR: A general framework allowing the direct manipulation of complex quantities: the separation between the real and the imaginary parts of a variable is avoided and the rules to integrate and derive probability density functions and characteristic functions are given, so that calculations may be carried out.

TL;DR: Signal to noise ratio (SNR) and linear prediction (LP) spectra are used as measures for comparing the performance of the proposed algorithm for the cases of one EOG channel and two EOG channels.

TL;DR: The optimality of the canonical variate analysis (CVA) method is demonstrated for both an open- and closedloop multivariable system with stochastic disturbances and the optimality is shown to hold in quite small samples of 100 time points.

TL;DR: The family of minimal state-space models of stationary processes with exogenous inputs in the absence of feedback are derived and a very natural block structure is introduced which is generically minimal.

TL;DR: A hierarchical approach using two Markov Random Fields on a holes topology reduces the drawbacks affecting classical approaches (i.e., over-segmentations) for extracting coastline in SAR images.

TL;DR: Generations for the Cramer-Rao Bound for the estimates of the parameters of a deterministic signal observed in additive and multiplicative noise which may be i.i.d. non-Gaussian or colored Gaussian are derived.

TL;DR: It is shown how, in the case of a finite-support analysis window and with the help of an overlap-add technique, the discrete Gabor transform can be used to determine Gabor's expansion coefficients for a signal whose support is not finite.

TL;DR: It is shown that the SS method is more robust to ill channel conditions than theCR method, but the CR method is generally more efficient in computation.

TL;DR: This work proposes a corner detector based on the 2-D Hilbert transform, which can be numerically evaluated by a separable 1-D digital filter and a peak detector to localise the extrema in the filter output.

TL;DR: Experimental results show that the genetic algorithm has advantage in the case where poles are close to the unit circle and for high-order filter problems.

TL;DR: The approach based on the maximum-likelihood principle is developed, it is shown that the estimators are based on higher-order statistics (HOS), and the unbiasedness of the estimator is proved and the Cramer-Rao bounds are given.

TL;DR: This paper defines precisely the multicorrelations and multispectra of complex signals and gives the definition for the practical case of discrete time and discrete frequency processes.

TL;DR: This paper deals with the radar detection of targets embedded in K-distributed clutter with partially correlated texture with a recursive implementation, which exploits the correlation properties of both texture and speckle components.