a new approach to multipath correction of constant modulus signals an adaptive digital filtering algorithm that can compensate for both frequency selective multipath and interference on constant envelope modulated signals is presented the method exploits the fact that multipath reception and various interference sources generate incidental amplitude modulation on the received signal a class of so called constant modulus performance functions is developed which sense this am term but are insensitive to the angle modulation simple adaptive algorithms for finite impulse response fir digital filters are developed which employ a gradient search of the performance function one of the resulting algorithms is simulated for the example of an fm signal degraded by specular multipath propagation substantial improvements in noise power ratio npr are observed e g 25 db with moderately rapid convergence time these results are then extended to include tonal interference on a fm signal and intersymbol interference on a qpsk data signal

blind deconvolution of multi input single output systems with binary sources the problem of blind source separation for multi input single output miso systems with binary inputs is treated in this paper our approach exploits the constellation properties of the successor values for each output sample in the absence of noise the successors of each output value form a characteristic finite set of clusters successor constellation the shape of this constellation is invariant of the predecessor value and it only depends on the last filter tap consequently the localization of the successors constellation can lead to the removal of the last filter tap thus reducing the length of the filter a process we call channel deflation based on the successor observation clustering soc we develop two algorithms for blind source separation soc 1 and soc 2 differing mainly on the required size of the data set furthermore the treatment of the system in the presence of noise is described using data clustering and data correction the problem of noise is attacked using a statistical mode based method moreover we correct the problem of misclassified observations using an iterative scheme based on the viterbi algorithm for the decoding of a hidden markov model hmm

multiple emitter location and signal parameter estimation processing the signals received on an array of sensors for the location of the emitter is of great enough interest to have been treated under many special case assumptions the general problem considers sensors with arbitrary locations and arbitrary directional characteristics gain phase polarization in a noise interference environment of arbitrary covariance matrix this report is concerned first with the multiple emitter aspect of this problem and second with the generality of solution a description is given of the multiple signal classification music algorithm which provides asymptotically unbiased estimates of 1 number of incident wavefronts present 2 directions of arrival doa or emitter locations 3 strengths and cross correlations among the incident waveforms 4 noise interference strength examples and comparisons with methods based on maximum likelihood ml and maximum entropy me as well as conventional beamforming are included an example of its use as a multiple frequency estimator operating on time series is included

determination of the dimension of a signal subspace from short data records a nested sequence of constant false alarm rate cfar hypothesis tests is presented for rank determination over short data records the procedure is based on the interpretation of sum of squares of singular values as energy in a particular subspace the cfar thresholds are set up based on distributions derived from matrix perturbation ideas expressions for probability of overestimation and underestimation are presented

an analytical constant modulus algorithm iterative constant modulus algorithms such as godard 1980 and cma have been used to blindly separate a superposition of cochannel constant modulus cm signals impinging on an antenna array these algorithms have certain deficiencies in the context of convergence to local minima and the retrieval of all individual cm signals that are present in the channel we show that the underlying constant modulus factorization problem is in fact a generalized eigenvalue problem and may be solved via a simultaneous diagonalization of a set of matrices with this new analytical approach it is possible to detect the number of cm signals present in the channel and to retrieve all of them exactly rejecting other non cm signals only a modest amount of samples is required the algorithm is robust in the presence of noise and is tested on measured data collected from an experimental set up

control solvability of interval systems of max separable linear equations abstract for a given matrix interval a a a and a given vector interval b b b the notation a x b if max represents an interval system of linear max separable equations several types of solvability of interval systems are known we define control solvability weak control solvability and weak universal solvability and give necessary and sufficient condition for an interval system to be solvable according to each definition

an updating algorithm for subspace tracking in certain signal processing applications it is required to compute the null space of a matrix whose rows are samples of a signal with p components the usual tool for doing this is the singular value decomposition however the singular value decomposition has the drawback that it requires o p sup 3 operations to recompute when a new sample arrives it is shown that a different decomposition called the urv decomposition is equally effective in exhibiting the null space and can be updated in o p sup 2 time the updating technique can be run on a linear array of p processors in o p time

direction of arrival estimation for constant modulus signals in many cases where direction finding is of interest the signals impinging on an antenna array are known to be phase modulated and hence to have a constant modulus cm this is a strong property by itself it is already sufficient for source separation and can be used to construct improved direction finding algorithms we first derive the relevant cramer rao bounds crbs for arbitrary array configurations and specialize to uniform linear arrays we then propose a simple suboptimal direction estimation algorithm in which the signals are separated using the cm property followed by direction finding on the decoupled signals compared with the esprit algorithm and the crb for arbitrary signals the algorithm shows good results

convergence of a generalized pulse spectrum technique gpst for inverse problems of 1 d diffusion equations in space time domain etude de la convergence d un algorithme iteratif de resolution des problemes inverses pour les equations de diffusion a une dimension dans un domaine d espace temps

a numerical algorithm for solving inverse problems of two dimensional wave equations abstract the new iterative numerical algorithm of the pulse spectrum technique pst is extended and developed to solve inverse problems of two dimensional linear wave equations it has the practical advantages of having the necessary data measured on a portion of the boundary only and no geometric limitation on the testing objects numerical simulations on several simple examples are carried out to test the feasibility and to study the general characteristics of this technique without the real measurement data it is found that pst does give excellent results even with a very coarse computational grid and it is as robust as in the one dimensional case

esprit a subspace rotation approach to estimation of parameters of cisoids in noise the application of a subspace invariance approach esprit to the estimation of parameters frequencies and powers of cisoids in noise is described esprit exploits an underlying rotational invariance of signal subspaces spanned by two temporally displaced data sets the new approach has several advantages including improved resolution over pisarenko s technique for harmonic retrieval

generalized pulse spectrum technique the generalized pulse spectrum technique gpst is a versatile and efficient iterative numerical algorithm for solving multiparameter inverse problems of a system of nonlinear partial differential equations here it is used to determine bulk modulus shear modulus and density variation from seismic data numerical simulations of simple but nontrivial examples are carried out on coarse uniform grids and adaptive grids to test and to study the general characteristics of gpst without the real measurements seismograms gpst does give reasonably good results it can be made more efficient by implementing parallelism automatic adaptive grids and methods of domain decomposition into the numerical algorithm

a widely convergent generalized pulse spectrum technique for the inversion of two dimensional acoustic wave equation an algorithm of the generalized pulse spectrum technique gpst is used for the inverse problems of two dimensional acoustic wave equation and a skill of saving computational cost is designed in choosing regularizing parameters to overcome the defect of the local convergence of the gpst a widely convergent gpst wcgpst is constructed numerical simulations are carried out to test the feasibility and to study the general characteristics of the wcgpst it is found that the wcgpst is not only as robust as the standard gpst but also possessing widely convergent region

the distribution and moments of the smallest eigenvalue of a random matrix of wishart type abstract given a random rectangular m n matrix with elements from a normal distribution what is the distribution of the smallest singular value to pose an equivalent question in the language of multivariate statistics what is the distribution of the smallest eigenvalue of a matrix from the central wishart distribution in the null case we give new results giving the distribution as a simple recursion this includes the more difficult case when n m is an even integer without resorting to zonal polynomials and hypergeometric functions of matrix arguments with the recursion one can obtain exact expressions for the density and the moments of the distribution in terms of functions usually no more complicated than polynomials exponentials and at worst ordinary hypergeometric functions we further elaborate on the special cases when n m 0 1 2 and 3 and give a numerical table of the expected values for 2 m 25 and 0 n m 25

waveform preserving blind estimation of multiple independent sources the problem of blind estimation of source signals is to estimate the source signals without knowing the characteristics of the transmission channel it is shown that the minimum variance unbiased estimates can be obtained if and only if the transmission channel can be identified blindly it is shown that the channel can be blindly identified if and only if there is not more than one gaussian source this condition suggests that waveform preserving blind estimation can be achieved over a wide range of signal processing applications including those cases in which the source signals have identical nongaussian distributions the constructive proof of the necessary and sufficient condition serves as a foundation for the development of waveform preserving blind signal estimation algorithms examples are presented to demonstrate the applications of the theoretical results

on blind beamforming for multiple non gaussian signals and the constant modulus algorithm a new method for blindly separating multiple cochannel non gaussian signals received by a sensor array is presented the method is based on a cumulant based least squares criterion that for identically distributed negative kurtosis signals is proven to be identical to the 2 2 constant modulus cm cost function commonly used by cm algorithms a computationally simple algorithm is proposed to minimize the criterion the algorithm performs well even when the number of samples is small thus allowing its application in dynamic environments e g moving emitters for the special case of two signals only the minimization is obtained analytically simulation results are included

non orthogonal joint diagonalization in the least squares sense with application in blind source separation approximate joint diagonalization of a set of matrices is an essential tool in many blind source separation bss algorithms a common measure of the attained diagonalization of the set is the weighted least squares wls criterion however most well known algorithms are restricted to finding an orthogonal diagonalizing matrix relying on a whitening phase for the nonorthogonal factor often such an approach implies unbalanced weighting which can result in degraded performance we propose an iterative alternating directions algorithm for minimizing the wls criterion with respect to a general not necessarily orthogonal diagonalizing matrix under some mild assumptions we prove weak convergence in the sense that the norm of parameters update is guaranteed to fall below any arbitrarily small threshold within a finite number of iterations we distinguish between hermitian and symmetrical problems using bss simulations results we demonstrate the improvement in estimating the mixing matrix resulting from the relaxation of the orthogonality restriction

sensor array signal processing via a procrustes rotations based eigenanalysis of the esprit data pencil the authors propose the procrustes rotation based esprit algorithm pro esprit for estimating characteristic parameters associated with a scene of radiating sources given data derived from two identical sensor arrays the x and y arrays which are displaced relative to one another pro esprit uses the inherent redundancy built into the esprit array system to compensate for deviations from the ideal array model this redundancy is manifested by two fundamental properties of the noiseless x and y data matrices comprising the esprit data pencil a they have the same row space the signal subspace and 2 they have the same column space the source subspace these properties are utilized by invoking two primary tools 1 the solution to the procrustes problem for optimally approximating an invariant subspace rotation and 2 the total least squares method for perturbing each of two estimates of a common subspace in a minimal fashion until the two perturbed spaces are the same

a wavelet multiscale homotopy method for the inverse problem of two dimensional acoustic wave equation abstract by introducing the wavelet multiscale method and the homotopy method to the inversion process for the velocity estimation problem of two dimensional acoustic wave equation in seismic prospecting a joint inversion method wavelet multiscale homotopy method is designed which is stable globally convergent and has the ability of noise suppression the results of numerical simulations and noise suppression tests indicate our method s effectiveness

penalty function based joint diagonalization approach for convolutive blind separation of nonstationary sources a new approach for convolutive blind source separation bss by explicitly exploiting the second order nonstationarity of signals and operating in the frequency domain is proposed the algorithm accommodates a penalty function within the cross power spectrum based cost function and thereby converts the separation problem into a joint diagonalization problem with unconstrained optimization this leads to a new member of the family of joint diagonalization criteria and a modification of the search direction of the gradient based descent algorithm using this approach not only can the degenerate solution induced by a null unmixing matrix and the effect of large errors within the elements of covariance matrices at low frequency bins be automatically removed but in addition a unifying view to joint diagonalization with unitary or nonunitary constraint is provided numerical experiments are presented to verify the performance of the new method which show that a suitable penalty function may lead the algorithm to a faster convergence and a better performance for the separation of convolved speech signals in particular in terms of shape preservation and amplitude ambiguity reduction as compared with the conventional second order based algorithms for convolutive mixtures that exploit signal nonstationarity

a widely convergent generalized pulse spectrum technique for the coefficient inverse problem of differential equations abstract a new algorithm of the generalized pulse spectrum technique gpst is constructed for the solution of coefficient inverse problems of differential equations and a skill of saving computational cost is designed in choosing regularizing parameters to overcome the defect of the local convergence of the gpst a widely convergent gpst is illustrated numerical simulations are carried out for solving the inverse problem of 1 d seismic prospection to test the feasibility without real measurement data the computational results show the method s effectiveness

an iterative numerical algorithm for solving multi parameter inverse problems of evolutional partial differential equations abstract the iterative numerical algorithm of the pulse spectrum technique pst is extended and developed to solve the multi parameter inverse problems of one dimensional evolutional partial differential equations wave equations or diffusion equations it has the practical advantages of universality economy of programing economy of data acquisition and economy of computing costs without the real measurement data numerical simulations are carried out only for the two parameter inverse problems of a one dimensional linear wave equation to test the feasibility and to study the general characteristics of the pst it is found that the pst does give excellent results and it is as robust as in the single parameter case

new processing techniques based on the constant modulus adaptive algorithm this paper presents three extensions of the constant modulus algorithm cma introduced in an earlier paper as a means of correcting degradations in constant enyelope waveforms as originally formulated the cma employs an fir filter with complex coefficients and accepts complex quadrature input data in this paper first a real input real coefficient version of the algorithm is shown to perform arbitrarily close to the fully complex version secondly the algorithm is extended for the enhancement of signals having a nonconstant but known envelope as might arise in data signals with pulse shaping lastly a multichannel version of cma wherein several observations are linearly combined is presented for joint adaptation of multiple filters this approach can be used for example as a means of spatial or polarization beamsteering to reject additive interferers and compensate for channel induced polarization rotation

on the schur decomposition of a matrix for parallel computation an algorithm to solve the eigenproblem for nonsymmetric matrices on an n n array of mesh connected processors isomorphic to the architecture described by brent and luk for symmetric matrices is presented this algorithm is a generalization of the classical jacobi method and as such holds promise for parallel architectures the rotational parameters for the nonsymmetric case are carefully analyzed many examples of a working program simulating the parallel architecture are given with experimental evidence of quadratic convergence

a continuous jacobi like approach to the simultaneous reduction of real matrices abstract the problem of simultaneous reduction of real matrices by either orthogonal similarity or orthogonal equivalence transformations is considered based on the jacobi idea of minimizing the sum of squares of the complementary part of the desired form to which matrices are reduced the projected gradient method is used in this paper it is shown that the projected gradient of the objective function can be formulated explicitly this gives rise to a system of ordinary differential equations that can be readily solved by numerical software the advantages of this approach are that the desired form to which matrices are reduced can be almost arbitrary and that if a desired form is not attainable then the limit point of the corresponding differential equation gives a way of measuring the distance from the best reduced matrices to the nearest matrices that have the desired form the general procedure for deriving these differential equations is discussed the framework can be generalized to the complex valued case some applications are given

fast subspace decomposition many recent signal processing techniques in various areas e g array signal processing system identification and spectrum estimation require a step of extracting the low dimensional subspace from a large space this step is usually called subspace decomposition which is conventionally accomplished by an eigendecomposition ed since an ed requires o m sup 3 flops for an m spl times m matrix it may represent a barrier to the real time implementation of these algorithms the authors present a fast algorithm for signal subspace decomposition which is termed fsd that exploits the special matrix structure low rank plus identity associated with signal subspace algorithms and requires only o m sup 2 d flops where d often spl lt m denotes the signal subspace dimension unlike most of alternatives the dimension of the signal subspace is not assumed known apriori and is estimated as part of the procedure moreover theoretical analysis has been conducted and its results show the strong consistency of the new detection scheme and the asymptotic equivalence between fsd and ed in estimating the signal subspace their new approach can also exploit other matrix structure common in signal processing areas e g toeplitz or hankel and thus achieve another order of computational reduction moreover it can be easily implemented in parallel to reduce further the computation time to as little as o md using o m simple processors

projection approximation subspace tracking subspace estimation plays an important role in a variety of modern signal processing applications we present a new approach for tracking the signal subspace recursively it is based on a novel interpretation of the signal subspace as the solution of a projection like unconstrained minimization problem we show that recursive least squares techniques can be applied to solve this problem by making an appropriate projection approximation the resulting algorithms have a computational complexity of o nr where n is the input vector dimension and r is the number of desired eigencomponents simulation results demonstrate that the tracking capability of these algorithms is similar to and in some cases more robust than the computationally expensive batch eigenvalue decomposition relations of the new algorithms to other subspace tracking methods and numerical issues are also discussed