dual h _ infty algorithms for signal processing application to speech enhancement this paper deals with the joint signal and parameter estimation for linear state space models an efficient solution to this problem can be obtained by using a recursive instrumental variable technique based on two dual kalman filters in that case the driving process and the observation noise in the state space representation for each filter must be white with known variances these conditions however are too strong to be always satisfied in real cases to relax them we propose a new approach based on two dual hinfin filters once a new observation of the disturbed signal is available the first hinfin algorithm uses the latest estimated parameters to estimate the signal while the second hinfin algorithm uses the estimated signal to update the parameters in addition as the hinfin filter behavior depends on the choice of various weights we present a way to recursively tune them this approach is then studied in the following cases 1 consistent estimation of the ar parameters from noisy observations and 2 speech enhancement where no a priori model of the additive noise is required for the proposed approach in each case a comparative study with existing methods is carried out to analyze the relevance of our solution

on the exact maximum likelihood estimation of gaussian autoregressive processes it is shown that the true maximum likelihood estimates of a gaussian zero mean ar process can be calculated the steps toward their obtainment with standard subroutines are calculating extrema are given the likelihood of the data is maximized in contrast to current approximate methods however the computational cost is significantly higher also the order p of the process is presumed known

on the error performance of the a_ n lattices we consider the root lattice a n and derive explicit recursive formulas for the moments of its voronoi cell these formulas enable accurate prediction of the error probability of lattice codes constructed from a n

on the geometry of positive maps in matrix algebras ii abstract geometric relations among the identity map the transpose map and the map x 1 n x tr x 1 n in the matrix algebra mn are illustrated providing parametric examples of maps which are not k 1 positive but which satisfy the matrix schwartz inequality of order k for 1 k n 1

an efficient and order recursive algorithm for estimating stationary arma models a new levinson type recursive algorithm that solves for the coefficients of the pth order arma model using o p2 operations is proposed the algorithm uses the fact that in the least squares optimal equation when the system is stationary the coefficient matrix is close to the toeplitz form the measures of closeness of a matrix to toeplitz form is determined by the concept of displacement rank

a look ahead levinson algorithm for general toeplitz systems the authors present an extension of levinson s algorithm that is guaranteed to be weakly stable for a large class of general toeplitz matrices namely those that do not have many consecutive ill conditioned leading principal submatrices the new algorithm adapts itself to the given toeplitz matrix by skipping over all the ill conditioned leading principal submatrices encountered during the solution process this is done by a look ahead strategy that monitors the condition of the leading principal submatrices and if necessary switches to a block step of suitable size the overhead of the look ahead algorithm is typically small compared to the classical levinson algorithm and in addition a reliable condition number estimate is produced

fast rls type algorithm for unbiased equation error adaptive iir filtering based on approximate inverse power iteration in this paper a fast approximate inverse power aip iteration is applied to compute recursively the total least squares tls solution for unbiased equation error adaptive infinite impulse response iir filtering which is established by approximating the well known inverse power iteration with galerkin method the aip algorithm is based on an interesting modification of the inverse power iteration in which the first entry of the parameter vector is constrained to the negative one the distinctive feature of the proposed algorithm lies in its high computational efficiency which is characterized by efficient computation of the fast gain vector fgv adaptation of the interesting modification of the inverse power iteration and rank one update of the augmented correlation matrix the shift structure of the input data vector is exploited to develop a fast algorithm for computing the gain vector this fgv algorithm can be implemented at a numerical cost lower than the well known fast kalman algorithm since the first entry of the parameter vector has been fixed as the negative one and the weight vector is updated along the input data vector a very efficient aip algorithm is obtained by using the fgv algorithm the proposed aip algorithm is of computational complexity o l per iteration moreover with no need to use the well known matrix inversion lemma the aip algorithm has another attractive feature of numerical stability the proposed algorithm is shown to have global convergence simulation examples are included to demonstrate the effectiveness of the proposed aip algorithm

analysis of a general recursive prediction error identification algorithm a general class of algorithms for recursive identification of stochastic dynamical systems is studied in this class the discrepancy between the measured output and the output predicted from previous data according to a candidate model the prediction error is minimized over the model set using a stochastic approximation approach it is proved that this class of methods has the same convergence properties as its off line counterparts under mild and general assumptions these assumptions do not for example include stationary conditions or conditions that the true system can be exactly represented within the model set the considered class of methods contains as special cases several well known algorithms other common recursive identification methods like the extended least squares method and the extended kalman filter can be interpreted as approximate prediction error methods with simplified gradient calculations therefore the approach taken here may also serve as a basis for unified description of many recursive identification methods

arma spectral estimation for noisy signals observed through a linear system spectral estimation for autoregressive moving average arma processes observed through a known linear system is considered an algorithm based on a system identification parameter estimation technique known as the recursive prediction error method is used for spectral estimation the algorithm is formulated in the state space to incorporate the constraints imposed by the linear system the linear system may represent the distortion introduced by the sensor or the channel several simulation examples are presented to illustrate the performance of the estimator

a recursive maximum likelihood algorithm for arma line enhancement optimal filtering of narrow band signals corrupted by noise requires an infinite impulse response iir filter a recursive maximum likelihood algorithm is proposed for adaptively adjusting the coefficients of an iir line enhancer the performance of this adaptive iir filter is analyzed and compared to that of a finite impulse response filter preliminary simulation results are presented to illustrate the behavior of the proposed algorithm under various signal to noise conditions

a fast recursive total least squares algorithm for adaptive fir filtering this paper proposes a new fast recursive total least squares n rtls algorithm to recursively compute the tls solution for adaptive finite impulse response fir filtering the n rtls algorithm is based on the minimization of the constrained rayleigh quotient c rq in which the last entry of the parameter vector is constrained to the negative one as analysis results on the convergence of the proposed algorithm we study the properties of the stationary points of the c rq the high computational efficiency of the new algorithm depends on the efficient computation of the fast gain vector fgv and the adaptation of the c rq since the last entry of the parameter vector in the c rq has been fixed as the negative one a minimum point of the c rq is searched only along the input data vector and a more efficient n rtls algorithm is obtained by using the fgv as compared with davila s rtls algorithms the n rtls algorithm saves the 6m number of multiplies divides and square roots mads the global convergence of the new algorithm is studied by lasalle s invariance principle the performances of the relevant algorithms are compared via simulations and the long term numerical stability of the n rtls algorithm is verified

the overdetermined recursive instrumental variable method a recursive instrumental variable algorithm is derived for the overdetermined case in which the number of instruments is greater than the number of estimated parameters in some applications the algorithm provides improved parameter estimation accuracy compared to the standard recursive instrumental variable method the performance of the algorithm is illustrated by simulation results

generalized choi maps in three dimensional matrix algebra we consider a class of positive linear maps in the three dimensional matrix algebra which are generalizations of the positive linear map constructed by choi in the relation with positive semidefinite biquadratic forms we find conditions for which such maps are completely positive completely copositive decomposable and two positive

instrumental variable methods for arma spectral estimation the modified yule walker technique of arma spectral estimation is shown to be a special case of the instrumental variable method of system identification several recursive instrumental variable algorithms are proposed for adaptive spectral estimation including a new algorithm for the overdetermined case an efficient lattice algorithm is presented for solving the modified yule walker equations given the sample correlation coefficients the performance of some of the algorithms is illustrated by simulation results

positive linear maps between matrix algebras which fix diagonals abstract we consider a class of positive linar maps from the n dimensional matrix algebra into itself which fix diagonal entries we show that they are expressed by hadamard products and study their decompositions into the sums of completely positive linear maps and completely copositive linear maps in the three dimensional case we show that every positive linear map in this type is decomposable and give an intrinsic characterization for the positivity of these maps when the involving coefficients are real numbers

accurate approximation of qam error probability on quasi static mimo channels and its application to adaptive modulation an accurate approximation for the conditional error probability on quasi static multiple input multiple output mimo antenna channels is proposed for a fixed channel matrix it is possible to accurately predict the performance of quadrature amplitude modulations qam transmitted over the mimo channel in presence of additive white gaussian noise the tight approximation is based on a simple union bound for the point error probability in the n dimensional real space instead of making an exhaustive evaluation of all pairwise error probabilities intractable in many cases a pohst or a schnorr euchner lattice enumeration is used to limit the local theta series inside a finite radius sphere the local theta series is derived from the original lattice theta series and the point position within the finite multidimensional qam constellation in particular we take into account the number of constellation facets hyperplanes that are crossing the sphere center as a direct application to the accurate approximation for the conditional error probability we describe a new adaptive qam modulation for quasi static multiple antenna channels

a series of absolutely indecomposable positive maps in matrix algebras abstract we provide examples of positive maps in m n c n 4 which cannot be decomposed into a sum of a 2 positive map and a 2 copositive map

special classes of positive and completely positive maps abstract characterizations are given for the positive and completely positive maps on n n complex matrices that leave invariant the diagonal entries or the k th elementary symmetric function of the diagonal entries 1 k n in addition it is shown that such a positive map is always decomposable if n 3 and this fails to hold if n 3 the real case is also considered

algebraic tools to build modulation schemes for fading channels a unified framework is presented in order to build lattice constellations matched to both the rayleigh fading channel and the gaussian channel the method encompasses the situations where the interleaving is done on the real components or on two dimensional signals in the latter case a simple construction of lattices congruent to the densest binary lattices with respect to the euclidean distance is proposed it generalizes in a sense to be clarified later the structural construction proposed by forney 1991 these constellations are next combined with coset codes the partitioning rules and the gain formula are similar to those used for the gaussian channel

optimal instrumental variable estimates of the ar parameters of an arma process the modified yule walker myw equations for estimating the ar parameters of an arma process are presented as a special case of an instrumental variable iv method the consistency and accuracy of the ar parameter estimates are studied it is shown that estimation accuracy increases monotonically with the number of myw equations for an optimal choice of the weighting matrix used in the least squares solution of these equations the asymptotic error covariance of the optimal iv method equals that of the prediction error method the results of this paper verify experimental results reported in the literature regarding the performance of the myw method and provide the necessary accuracy analysis furthermore they suggest several simple asymptotically efficient multistep algorithms for estimating the ar parameters which are presented in a companion paper

an optimal instrumental variable method for arma spectral estimation a multistep iterative and fast recursive algorithm for autoregressive moving average arma spectral estimation is presented the ar parameters of an arma process are estimated using the extended instrumental variable eiv method the optimal choice of instruments prefilter and weighting matrix is investigated a bootstrapping procedure that has computational convenience is proposed for the algorithm the statistical analysis and experiments show that the optimal iv estimate is unbiased consistent efficient asymptotically normal and equivalent to the maximum likelihood ml estimate and the prediction error pe estimate and the proposed algorithm has the advantages of sharper resolution less frequency bias and better efficiency of convergence

constructing positive maps from block matrices positive maps are useful for detecting entanglement in quantum information theory any entangled state can be detected by some positive map in this paper the relation between positive block matrices and completely positive trace preserving maps is characterized consequently a new method for constructing decomposable maps from positive block matrices is derived in addition a method for constructing positive but not completely positive maps from hermitian block matrices is also obtained

error performance of multidimensional lattice constellations part ii evaluation over fading channels this is the second part of a two part series of papers where the error performance of multidimensional lattice constellations with signal space diversity ssd is investigated in part i following a novel combinatorial geometrical approach which is based on parallelotope geometry we have presented an exact analytical expression and two closed form bounds for the symbol error probability sep in additive white gaussian noise awgn in the present part ii we extend the analysis and present a novel analytical expression for the frame error probability fep of multidimensional lattice constellations over nakagami m fading channels as the fep of infinite lattice constellations is lower bounded by the sphere lower bound slb we propose the sphere upper bound sub for block fading channels furthermore two novel bounds for the fep of multidimensional lattice constellations over block fading channels named multiple sphere lower bound mslb and multiple sphere upper bound msub are presented the expressions for the slb and sub are given in closed form while the corresponding ones for mslb and msub are given in closed form for unitary block length numerical and simulation results illustrate the tightness of the proposed bounds which can be efficiently used to set the performance limits on the fep of lattice constellations of arbitrary structure dimension and rank

a recursive maximum likelihood algorithm for arma spectral estimation a spectral estimation technique is presented for autoregressive moving average arma processes the technique is based on a parameter estimation technique known as the rec ursive maximum likelihood rml method the recursive spectral estimation algorithm is presented and its asymptotic properties are discussed simulation results are presented to illustrate the performance of the estimator for various types of data

new algebraic constructions of rotated z sup n lattice constellations for the rayleigh fading channel in this correspondence we present various families of full diversity rotated z sup n lattice constellations based on algebraic number theory constructions we are able to give closed form expressions of their minimum product distance using the corresponding algebraic properties

estimation of all pole model parameters from noise corrupted sequences least squares estimates of the parameters of all pole signal models are biased when applied to noise corrupted all pole sequences this bias has been shown to be proportional to the inverse of the signal to noise ratio an autocorrelation like procedure is shown to increase under some circumstances the signal to noise ratio while retaining the pole locations when the noise is white the signal to noise ratio improvement varies for each mode in the all pole sequence with modes corresponding to pole locations close to the unit circle showing the most improvement a signal to noise ratio cutoff exists for each mode below which no improvement can be obtained this cutoff depends on the proximity of the pole location to the unit circle approaching zero as the pole nears the origin this cutoff corresponds to the point at which a mode s spectral value just equals the level of the noise floor this procedure can be applied recursively until an acceptable signal to noise ratio is obtained the all pole parameters can then be estimated from the processed sequence using least squares estimation

asymptotically optimal estimation of ma and arma parameters of non gaussian processes from high order moments a description is given of an asymptotically minimum variance algorithm for estimating the ma moving average and arma autoregressive moving average parameters of non gaussian processes from sample high order moments the algorithm uses the statistical properties covariances and cross covariances of the sample moments explicitly a simpler alternative algorithm that requires only linear operations is also presented the latter algorithm is asymptotically minimum variance in the class of weighted least squares algorithms

good lattice constellations for both rayleigh fading and gaussian channels recent work on lattices matched to the rayleigh fading channel has shown how to construct good signal constellations with high spectral efficiency we present a new family of lattice constellations based on complex algebraic number fields which have good performance on rayleigh fading channels some of these lattices also present a reasonable packing density and thus may be used at the same time over a gaussian channel conversely we show that particular versions of the best lattice packings d sub 4 e sub 6 e sub 8 k sub 12 spl lambda sub 16 spl lambda sub 24 constructed from totally complex algebraic cyclotomic fields present better performance over the rayleigh fading channel the practical interest in such signal constellations rises from the need to transmit information at high rates over both terrestrial and satellite links some further results in algebraic number theory related to ideals and their factorization are presented and the decoding algorithm used with these lattice constellations are illustrated together with practical results

comparison of some instrumental variable methods consistency and accuracy aspects a general analysis of various instrumental variable methods is given the investigation includes methods for open loop operation as well as methods designed for closed loop operation the instrumental variables are formed as different combinations of inputs delayed inputs delayed outputs filtered inputs and external setpoint variations necessary conditions as well as sufficient conditions for consistency are explicitly derived and discussed the paper also includes some simple counter examples to general consistency for some common instrumental variable methods also asymptotic accuracy properties are reviewed it is e g studied how the use of filters will influence the covariance matrix of the parameter estimates

error performance of multidimensional lattice constellations part i a parallelotope geometry based approach for the awgn channel multidimensional lattice constellations which present signal space diversity ssd have been extensively studied for single antenna transmission over fading channels with focus on their optimal design for achieving high diversity gain in this two part series of papers we present a novel combinatorial geometrical approach based on parallelotope geometry for the performance evaluation of multidimensional finite lattice constellations with arbitrary structure dimension and rank in part i we present an analytical expression for the exact symbol error probability sep of multidimensional signal sets and two novel closed form bounds named multiple sphere lower bound mlsb and multiple sphere upper bound msub part ii extends the analysis to the transmission over fading channels where multidimensional signal sets are commonly used to combat fading degradation numerical and simulation results show that the proposed geometrical approach leads to bounds that can be used for the performance evaluation and the design of multidimensional lattice constellations both in additive white gaussian noise awgn and fading channels

simple iterative methods to exploit the signal space diversity signal space diversity is a power and bandwidth efficient diversity technique to exploit the signal space diversity joint maximum likelihood ml detection at the receiver is usually needed where the complexity grows exponentially with the dimension of the lattice in this letter we propose a serial concatenated scheme and two simple iterative methods to exploit the signal space diversity the simple iterative methods are based on the idea of soft interference cancellation the first iterative method is based on a vector gaussian approximation while the second one is based on a scalar gaussian approximation the complexity of the first iterative method grows cubically with the dimension of the lattice and the simulations show that its performance approaches that of the optimal maximum a posteriori detection method the complexity of the second iterative method grows linearly with the dimension of the lattice and the simulations show that when the dimension of the lattice n 32 at bit error rate 10 sup 5 the performance gap between the rayleigh fading channel and the gaussian channel is only 0 3 db

on the identification of systems from data measurements using arma lattice models methods for the autoregressive moving average arma modeling of digital systems using a two channel autoregressive ar lattice are presented one method has the advantage that one half of the lattice parameters are zero when the system s excitation signal is white noise

universal bound on the performance of lattice codes we present a lower bound on the probability of symbol error for maximum likelihood decoding of lattices and lattice codes on a gaussian channel the bound is tight for error probabilities and signal to noise ratios of practical interest as opposed to most existing bounds that become tight asymptotically for high signal to noise ratios the bound is also universal it provides a limit on the highest possible coding gain that may be achieved at specific symbol error probabilities using any lattice or lattice code in n dimensions in particular it is shown that the effective coding gains of the densest known lattices are much lower than their nominal coding gains the asymptotic as n spl rarr spl infin behavior of the new bound is shown to coincide with the shannon 1948 limit for gaussian channels

nonlinear filtering methods for harmonic retrieval and model order selection in gaussian and non gaussian noise this paper addresses the problem of high resolution parameter estimation harmonic retrieval and model order selection for superimposed sinusoids the harmonic retrieval problem is analyzed using a nonlinear parameter estimation approach estimation is performed using several nonlinear estimators with signals embedded in white and colored gaussian noise simulation results demonstrate that the nonlinear filters perform close to the cramer rao bound model order selection is performed in gaussian and non gaussian noise the problem is formulated using a multiple hypothesis testing approach with assumed known a priori probabilities for each hypothesis parameter estimation is performed using the extended kalman filter when the noise is gaussian the extended high order filter ehof is implemented in non gaussian noise simulation results demonstrate excellent performance in selecting the correct model order and estimating the signal parameters

square root covariance ladder algorithms square root normalized ladder algorithms provide an efficient recursive solution to the problem of multichannel autoregressive model fitting a simplified derivation of the general update formulas for such ladder forms is presented and is used to develop the growing memory and sliding memory covariance ladder algorithms new ladder form realizations for the identified models are presented leading to convenient methods for computing the model parameters from estimated reflection coefficients a complete solution to the problem of possible singularity in the ladder update equations is also presented

lattice implementation of some recursive parameter estimation algorithms linear dynamic models of plants are usually parametrized by the coefficients of difference equations lattice structures and their reflection coefficients provide an alternative parametrization that offers several advantages including numerical robustness computational efficiency and ease of hardware implementation the recursive square root normalized lattice versions of the following well known parameter estimation algorithms are presented recursive least squares recursive instrumental variable extended least squares and recursive maximum likelihood

algebraic lattice constellations bounds on performance in this work we give a bound on performance of any full diversity lattice constellation constructed from algebraic number fields we show that most of the already available constructions are almost optimal in the sense that any further improvement of the minimum product distance would lead to a negligible coding gain furthermore we discuss constructions minimum product distance and bounds for full diversity complex rotated z i sup n lattices for any dimension n which avoid the need of component interleaving

a class of atomic positive linear maps in matrix algebras we find a large class of atomic positive linear maps between n dimensional matrix algebras and study their properties in connection with complete positivity 2 positivity and schwarz inequality

information tradeoffs in using the sample autocorrelation function in arma parameter estimation this paper considers bounds on the statistical efficiency of estimators of the poles and zeros of an arma process based on estimates of the process autocorrelation function acf special attention is paid to autoregressive ar and ar plus white noise processes it is seen that reducing the arma process data to a given set of consecutive lags of the popular lagged product acf estimates prior to parameter estimation increases cramer rao bounds on the generalized error covariance a parametric study of the bound deterioration for some illustrative signal and noise situations reveals some empirical strategies for choosing acf estimate lags to preserve statistical information analysis is based on the relative information index rii 2 and derivations of the large sample fisher s information matrix for the raw data and for the lagged product acf estimate of an arma process are included

parametric estimation of the cross power spectral density a new cross spectral analysis procedure is proposed for the parametric estimation of the relationship between two time sequences in the frequency domain in this method the two observable outputs are modeled as a pair of autoregressive moving average and moving average armama models under the assumption that the two outputs are driven by a common input and independent ones simultaneously cross and auto power spectral densities psds of a pair of armama models can be derived as forms of rational polynomial functions the coefficients of these functions can be estimated from the cross correlation function or the auto correlation functions of the two observed sequences by using the method presented in this paper the main advantage of the present procedure is that the physical parameters of an unknown system can be easily estimated from the coefficients of the cross and auto psd functions to illustrate the effectiveness of the proposed procedure numerical and practical examples of a mechanical vibration problem are analyzed the results show that the proposed procedure gives accurate cross and auto psd estimates moreover the physical properties of the unknown system can be well estimated from the obtained cross and auto psds

on the symbol error rates for signal space diversity schemes over a rician fading channel a signal space diversity ssd scheme is one of techniques to achieve diversity gain in fading channels this method consists of two key operations constellation rotation and component wise interleaving because of these operations the decision boundaries for the ssd are no longer perpendicular and thus different coordination approaches are required for the analysis of error rates compared to conventional rectangular coordinates in this letter we derive an exact expression of the symbol error rate for the ssd scheme in rician fading channels with m qam and m psk by defining the ratio of the standard deviation of the inphase and quadrature components we introduce a new signal model for the ssd based on this signal model we can compute the exact symbol error rate using polar coordinates the computer simulation results confirm the accuracy of our analysis for fading channels

performance evaluation of the modified yule walker estimator the modified yule walker myw equations are often used to estimate the autoregressive parameters of time series the asymptotic estimation error variance of these parameters can be computed using a formula developed for the instrumental variable method the performance of the myw estimator is studied by evaluating this formula for different signal to noise ratios and process bandwidths as a function of the number of equations used by the estimator the cramer rao lower bound for the estimation error variance is computed for comparison the optimally weighted myw estimator is briefly discussed

filter order selection in adaptive maximum likelihood estimation the choice of the number of parameters is an important problem in adaptive filtering in the general ground of maximum likelihood ml parameter estimation akaike criterion has proved to be a very useful tool significant results and the importance of the kullback information measure as the implicit measure of distance associated with ml estimation are discussed application of these results to adaptive filtering is then considered for time invariant and time varying parametric models the corresponding practical forms of akaike criterion are given in the time varying case this criterion may be used as a way to fit the model into the nonstationarity of an exact filter the possible generalization of the akaike criterion to non ml estimation is discussed and application is made for weighted least squares estimation a more general approach to the nonstationary case is given by assuming a linear stochastic model for the vector of the filter coefficients in which case the ml estimate is computed recursively using the kalman filtering equation then the random sequence of kalman gains is close to a deterministic average sequence this sequence may be precomputed and gives an optimal choice for the gain matrices in an adaptive gradient type filter

application of the lattice filter to robust estimation of ar and arma models a method for estimating the parameters of an autoregressive moving average arma model is proposed a p q stage lattice whitening filter is used to obtained consistent estimates of the ar parameters of an arma p p model it is shown that a set of ma parameter estimates can also be obtained using this approach with little added computation the method is used for estimation of the ar parameters of an arma process in additive white noise it is shown that the lattice filter is also useful in robust estimation of the ar parameter on an arma process with additive outliers

real domain decoder for full rate full diversity stbc with multidimensional constellations in this letter we present a new maximum likelihood ml decoding algorithm for space time block codes stbcs that employ multidimensional constellations we start with a lattice representation for stbcs which transforms complex channel models into real matrix equations based on the lattice representation we propose a new decoding algorithm for quasi orthogonal stbcs qo stbc which allows simple ml decoding with performance identical to the conventional ml decoder multidimensional rotated constellations are constructed for the qo stbcs to achieve full diversity as a consequence for quasi orthogonal designs with an arbitrary number of transmit antennas n nges4 the proposed decoding scheme achieves full rate and full diversity while reducing the decoding complexity from o mc n 2 to o mc n 4 in a mc qam constellation

classification of digital amplitude phase modulated signals in time correlated non gaussian channels in this paper a new algorithm is proposed for the classification of digital amplitude phase modulated signals in flat fading channels with time correlated non gaussian noise the first order statistics of the additive noise is modeled by a gaussian mixture distribution and an autoregressive ar process is used to model the time correlation the proposed classifier involves the use of a whitening filter necessary to reduce the complexity of the classification process and maximum likelihood classification for the estimation of the whitening filter coefficients a new blind technique that is based on the use of a robust h filter is developed after whitening the received signal following a composite hypothesis testing approach the unknown fading and noise distribution parameters are estimated results are presented which show that when the noise process is time correlated non gaussian the proposed classifier outperforms maximum likelihood classifiers developed under the assumption that the noise process is either white non gaussian or white gaussian it is also shown that when the noise process is white gaussian the proposed classifier s performance closely approaches that of the maximum likelihood classifier developed for white gaussian noise channels

recursive lattice forms for spectral estimation a class of lattice prediction filters is proposed for high resolution spectral estimation the square root normalized lattice recursions are used to estimate a set of reflection coefficients from the data the lattice variables determine the coefficients of a least squares predictor from which the spectrum can be evaluated the prewindowed and sliding window covariance cases are considered for both ar and arma spectra the behavior of the proposed spectral estimator is illustrated by simulation results

indecomposable positive maps in low dimensional matrix algebras abstract we classify a series of positive maps in low dimensional matrix algebras with respect to the degree of indecomposability and provide stronger indecomposable maps in m n we also present a basic result about the atomic property

a parameter estimation approach to estimation of frequencies of sinusoids it is shown that with uniform sampling a sample of the sum of m sinusoids at time nt can be uniquely expressed as a linear combination of the 2m samples at times n 1 t n 2m t the 2m coefficients of linear combination are also the coefficients of a 2m order polynomial whose roots are e pmj omega i omega i being the frequencies of the sinusoids given the samples of sinusoids plus noise a consistent estimate of the 2m coefficients are obtained by the instrumental variable method of parameter estimation it is also possible to track time varying frequencies by a recursive algorithm that exponentially weighs out the past data simulation examples of one and two sinusoids are given

on instrumental variable estimation of sinusoid frequencies and the parsimony principle multiple sinusoids in noise can be modeled as an arma process with the ar parameters satisfying certain symmetry relations according to the parsimony principle the constraints on the ar parameters should he taken into account to get improved estimation accuracy it is shown in this note that when estimating the ar parameters by a general instrumental variable method such a parsimony does not necessarily apply however the parsimony principle does hold when an optimal instrumental variable method is used

joint multiuser detection of multidimensional constellations over fading channels we investigate the error performance of multidimensional constellations in the multiple access and broadcast channels more specifically we provide closed form expressions for the pairwise error probability pep of the joint maximum likelihood detection for multiuser signaling in the presence of additive white gaussian noise and rayleigh fading arbitrary numbers of users and multidimensional signal sets are assumed while the provided formula for the pep is a function of the dimension wise distances of the multidimensional constellation furthermore a useful upper bound on the average symbol error probability is also obtained through the union bound the analysis is applied to the sparse code multiple access systems the analytical results are validated successfully through simulations and show their importance in the multidimensional constellation design

bi iteration recursive instrumental variable subspace tracking and adaptive filtering in this paper we propose a class of fast sequential bi iteration singular value bi svd subspace tracking algorithms for adaptive eigendecomposition of the cross covariance matrix in the recursive instrumental variable riv method of system identification these algorithms can be used for riv subspace processing of signals in unknown correlated gaussian noise realizations with o nr sup 2 and o nr operations per time step are described where n is the input vector dimension and r is the number of dominant singular values and vectors to be tracked the algorithms are solely based on passive givens plane rotations and standard matrix vector multiplications the matrix inversion lemma is not used the application and performance of the algorithms is demonstrated in a low rank riv subspace adaptive filtering context