an iterative algorithm for single frequency estimation an algorithm for the estimation of the frequency of a complex sinusoid in noise is proposed the estimator consists of multiple applications of lowpass filtering and decimation frequency estimation by linear prediction and digital heterodyning the estimator has a significantly reduced threshold relative to existing phase based algorithms and performance close to that of maximum likelihood estimation in addition the mean squared error performance is within 0 7 db of the cramer rao bound crb at signal to noise ratios snrs above threshold unlike many autocorrelation and phase based methods the proposed algorithm s performance is uniform across a frequency range of spl pi to spl pi the computational complexity of the algorithm is shown to be favorable compared with maximum likelihood estimation via the fast fourier transform fft algorithm when significant zero padding is required

exponential parameter estimation in the presence of known components and noise in the determination of the natural modes of an electromagnetic scatterer the measured time series will contain desired information noise and quite often known transient components introduced by the excitation source or measuring equipment this paper describes a linearly constrained total least squares lctls prony method for extracting the exponential model parameters from observed transient data for such problems the tls criterion yields better parameter estimates than ls moreover the incorporation of known signal information via constraints leads to even greater improvements in performance mathematical connections between lctls prony and a tls variation of time series deflation tsd are used to derive constraints for higher order excitation poles also we use tsd concepts to derive numerically superior data transformations for lctls simulation studies involving idealized test data and synthetic scattering response data of a perfectly conducting sphere demonstrate the advantages of the method

nonlinear decoupling via feedback a differential geometric approach the paper deals with the nonlinear decoupling and noninteracting control problems a complete solution to those problems is made possible via a suitable nonlinear generalization of several powerful geometric concepts already introduced in studying linear multivariable control systems the paper also includes algorithms concerned with the actual construction of the appropriate control laws

techniques for the efficient evaluation of two dimensional autocorrelation functions the evaluation of the autocorrelation of multidimensional sequences using the fft is investigated the bidimensional case is treated in detail not only because it is of special interest for the applications but also because the reasoning used for this case can be applied to higher dimension signals the analysis confirms that the use of the proposed frequency domain techniques leads to significant computation time savings

robust tracking in nonlinear systems a nonlinear feedback multivariable controller is used to implement multivariable tracking in a nonlinear system the tracking error is measured by a general function of system state and the input command the controller is robust in the sense that the tracking error is ultimately bounded in the presence of modeling errors free parameters which affect the form of the controller allow flexibility in determining such factors as the size of the ultimate bound the rate of error decay excursion of the control conditions on the class of modeling errors and the level of system gain restrictive assumptions on the structure of the model and the modeling errors are required but they are treated in a transformation framework which allows the generalization of similar conditions which appear in the prior literature these assumptions hold for robotic manipulators this application is investigated at some length and it appears that the resulting control scheme may have advantages over others which have been proposed in the robotics literature

extending the threshold and frequency range for phase based frequency estimation standard phase based frequency estimation has a threshold that is high is frequency dependent and does not decrease with increasing signal length these problems are solved by processing the signal with a highly overlapped filter bank before applying phase based frequency estimation by exploiting decimation a closed form matrix inversion and cascades of simple filter banks the computational complexity is kept low

analysis of modified smi method for adaptive array weight control the authors characterize the performance of the diagonally loaded sample matrix inverse smi algorithm versus the number k of snapshots used in the covariance matrix estimate by providing o 1 k statistics bias and variance of the array weights output powers and output power ratios such as signal to interference noise ratio sinr and inr the approach accommodates wideband signals monte carlo simulations verify the theoretical analysis

nonlinear autoregressive and nonlinear autoregressive moving average model parameter estimation by minimizing hypersurface distance the least squares ls can be used for nonlinear autoregressive nar and nonlinear autoregressive moving average narma parameter estimation however for nonlinear cases the ls results in biased parameter estimation due to its assumption that the independent variables are noise free the total least squares tls is another method that can used for nonlinear parameter estimation to increase the accuracy of the ls because it specifically accounts for the fact that the independent variables are noise corrupted tls has its own limitations however mainly because it is difficult for the method to isolate noise from the signal components we present a new method that is based on minimization of hypersurface distance for accurate parameter estimation for nar and narma models computer simulation examples show that the new method results in far more accurate nar and narma model parameter estimates than do either the ls and tls with noise that is either white or colored and retains its high accuracy even when the signal to noise ratio snr is as low as 10 db

approximate maximum likelihood algorithms for two dimensional frequency estimation of a complex sinusoid starting with the maximum likelihood ml formulation three iterative algorithms for approximate ml frequency estimation of a two dimensional 2 d complex sinusoid in white gaussian noise are developed mean and variance analyses of the proposed methods are provided which show that they are approximately unbiased and their performance achieves cramer rao lower bound crlb at sufficiently high signal to noise ratio snr conditions computer simulation results are included to corroborate the theoretical development as well as to contrast the performance of the proposed algorithms with kay s estimators and the crlb

edge coloring and stopping sets analysis in product codes with mds components we consider non binary product codes with mds components and their iterative row column algebraic decoding on the erasure channel both independent and block erasures are considered in this paper a compact graph representation is introduced on which we define double diversity edge colorings via the rootcheck concept an upper bound of the number of decoding iterations is given as a function of the graph size and the color palette size m then we propose a differential evolution edge coloring algorithm that produces colorings with a large population of minimal rootcheck order symbols the complexity of this algorithm per iteration is o m aleph for a given differential evolution parameter aleph where m aleph itself is small with respect to the huge cardinality of the coloring ensemble stopping sets of a product code are defined in the context of mds components and a relationship is established with the graph representation a full characterization of these stopping sets is given up to a size d 1 2 where d is the minimum hamming distance of the mds component code the performance of mds based product codes with and without double diversity coloring is analyzed in presence of both the block and the independent erasures in the latter case ml and iterative decoding are proven to coincide at small channel erasure probability furthermore numerical results show excellent performance in presence of unequal erasure probability due to double diversity colorings

accurate and computationally efficient tensor based subspace approach for multidimensional harmonic retrieval in this paper parameter estimation for r dimensional r d sinusoids with r 2 in additive white gaussian noise is addressed with the use of tensor algebra and principal singular vector utilization for modal analysis the sinusoidal parameters at one dimension are first accurately estimated according to an iterative procedure which utilizes the linear prediction property and weighted least squares the damping factors and frequencies in the remaining dimensions are then solved such that pairing of the r d parameters is automatically achieved algorithm modification for a single r d tone is made and it is proved that the frequency estimates are asymptotically unbiased while their variances approach cramer rao lower bound at sufficiently high signal to noise ratio conditions computer simulations are also included to compare the proposed approach with conventional r d harmonic retrieval schemes in terms of mean square error performance and computational complexity

sinusoidal frequency estimation by signal subspace approximation an efficient fourier transform based method that avoids eigenvector computation is proposed for approximating the signal subspace the resulting signal subspace estimate can be used directly to define a music type frequency estimator or as a very good initial guess in context with adaptive or iterative eigenvector computation schemes at low signal to noise ratios the approximation yields better results than exact music it is also more robust than music against overestimating the number of sinusoids some variations of the basic method are briefly discussed

the weight hierarchy of some reducible cyclic codes the generalized hamming weights ghws of linear codes are fundamental parameters the knowledge of which is of great interest in many applications however to determine the ghws of linear codes is difficult in general in this paper we study the ghws for a family of reducible cyclic codes and obtain the complete weight hierarchy in several cases this is achieved by extending the idea of yang et al into higher dimension and by employing some interesting combinatorial arguments it shall be noted that these cyclic codes may have arbitrary number of nonzeros

exact maximum likelihood parameter estimation of superimposed exponential signals in noise a unified framework for the exact maximum likelihood estimation of the parameters of superimposed exponential signals in noise encompassing both the time series and the array problems is presented an exact expression for the ml criterion is derived in terms of the linear prediction polynomial of the signal and an iterative algorithm for the maximization of this criterion is presented the algorithm is equally applicable in the case of signal coherence in the array problem simulation shows the estimator to be capable of providing more accurate frequency estimates than currently existing techniques the algorithm is similar to those independently derived by kumaresan et al in addition to its practical value the present formulation is used to interpret previous methods such as prony s pisarenko s and modifications thereof

a generalized weighted linear predictor frequency estimation approach for a complex sinusoid based on linear prediction and weighted least squares three simple iterative algorithms for frequency estimation of a complex sinusoid in additive white noise are devised the proposed approach which utilizes the first order as well as higher order linear prediction terms simultaneously but does not require phase unwrapping can be considered as a generalized version of the weighted linear predictor frequency estimator in particular convergence as well as mean and variance analysis of the most computationally efficient frequency estimator namely gwlp 2 are provided computer simulations are included to contrast the performance of the proposed algorithms with several conventional computationally attractive frequency estimators and crame spl acute r rao lower bound for different frequencies observation lengths and signal to noise ratios

the most efficient implementation of the iqml algorithm the work by clark and scharf 1992 showed a new implementation of the iqml iterative quadratic maximum likelihood algorithm which requires at each iteration computational flops of order n sup 2 where n is the dimension of signal vector or length of data sequence they also indicated that the implementation of other related algorithms such as the steiglitz mcbride 1965 algorithm would also require order n sup 2 computations the present author gives a better way of implementation which requires computational flops of the order n this better way of implementation is shown in detail for the iqml algorithm following the same idea shown in the present paper one can also straightforwardly design the order n implementation of the steiglitz mcbride algorithm the present implementation is also the most efficient in that no implementation can be made less than order n sup 2

weighted least squares design and characterization of complex fir filters the paper presents two novel weighted least squares methods for the design of complex coefficient finite impulse response fir filters to attain specified arbitrary multiband magnitude and linear or arbitrary phase responses these methods are computationally efficient requiring only the solution of a toeplitz system of n linear equations for an n length filter that can be obtained in o n sup 2 operations illustrative filter design examples are presented

list decoding tensor products and interleaved codes we design the first efficient algorithms and prove new combinatorial bounds for list decoding tensor products of codes and interleaved codes we show that for every code the ratio of its list decoding radius ldr to its minimum distance stays unchanged under the tensor product operation rather than squaring as one might expect this gives the first efficient list decoders and new combinatorial bounds for some natural codes including multivariate polynomials where the degree in each variable is bounded we show that for every code its ldr remains unchanged under m wise interleaving for an integer m this generalizes a recent result of dinur et al in proceedings of the 40th acm symposium on theory of computing stoc 08 2008 pp 275 284 who proved such a result for interleaved hadamard codes equivalently linear transformations using the notion of generalized hamming weights we give better list size bounds for both the tensoring and interleaving of binary linear codes by analyzing the weight distribution of these codes we reduce the task of bounding the list size to one of bounding the number of close by low rank codewords for decoding linear transformations using rank reduction together with other ideas we obtain list size bounds that are tight over small fields our results give better bounds on the ldr than what is obtained from the johnson bound and yield rather general families of codes decodable beyond the johnson radius

continuous state feedback guaranteeing uniform ultimate boundedness for uncertain dynamic systems we consider a dynamic system containing uncertain elements only the set of possible values of these uncertainties is known based on this information a class of state feedback controls is proposed in order to guarantee uniform ultimate boundedness of every system response within an arbitrarily small neighborhood of the zero state these feedback controls are continuous functions of the state

list decoding of crisscross errors in this paper list decoding of crisscross errors in arrays over finite fields is considered for this purpose the so called cover metric is used where the cover of a matrix is a set of rows and columns which contains all non zero elements of the matrix a johnson like upper bound on the maximum list size in the cover metric is derived showing that the list of codewords has polynomial size up to a certain radius furthermore a simple list decoding algorithm for a known optimal code construction is presented which decodes errors in the cover metric up to our upper bound these results reveal significant differences between the cover metric and the rank metric and show that the cover metric is more suitable for correcting crisscross errors

analysis of the variance threshold of kay s weighted linear predictor frequency estimator a theoretical approximation for the variance of kay s weighted linear predictor frequency estimator is derived from this expression an inequality describing the variance threshold of the estimator is found the window weights are then optimized to improve the variance numerical simulations demonstrate that the variance approximations are valid for medium to high signal to noise ratios or for large numbers of samples

exponential stability of a class of nonlinear dynamical systems with uncertainties abstract exponential stability for a class of nonlinear dynamical systems with uncertainties is investigated based on the stability of the nominal systems a new approach to synthesizing a class of continuous state feedback controllers for uncertain nonlinear dynamical systems is proposed by such a class of feedback controllers we can guarantee exponential stability of uncertain nonlinear dynamical systems our approach can give a clear insight into system analysis finally an illustrative example is given to demonstrate the utilization of the approach developed in this paper

frequency estimation from proper sets of correlations as a complement to the periodogram low complexity frequency estimators are of interest one such estimator is based on prony s method and rely on phase information of the auto correlations without prior knowledge of the frequency e g a given frequency interval the frequency cannot be unambiguously estimated from a single correlation only we introduce a new method of phase unwrapping using an arbitrary number more than one of correlations from this arbitrary set of correlations we propose a weighted average estimator we derive the asymptotic performance and show how the correlation lags should be properly chosen from a design aspect there is often a restriction of using a fixed number of computations in addition we therefore propose a strategy to find a proper set of correlation lags subject to a given computational complexity finally simulation results that lend support to the theoretical findings are included

characterizing three dimensional wavefields with high resolution spectra three dimensional spectra frequency x wavenumber y wavenumber or f kx ky spectra can be used to determine the frequency content velocity and direction of waves entering an array of receivers this information is important in detecting aliasing problems understanding coherent noise designing arrays and determining parameters for coherent noise filters because of the limited spatial dimensions of most arrays the discrete fourier transform produces an estimate of the three dimensional 3 d spectrum with severe wavenumber distortion we extend a 2 d hybrid spectral estimation method to three dimensions by combining a temporal fourier transform with a spatial 2 d maximum entropy spectral estimation technique the method produces f kx ky spectra with higher wavenumber resolution and less spectral distortion than corresponding 3 d fourier spectra the 2 d maximum entropy spectral estimation algorithm uses a sequence of fourier transforms to extrapolate the estimated autocorrelation function of the data

significance of group delay functions in spectrum estimation a method of spectrum estimation using group delay functions is proposed this method exploits the additive property of the fourier transform ft phase to extract spectral information of the signal in the presence of noise the phase is generally featureless due to random polarity and wrappings but the group delay function can be processed to derive significant information such as peaks in the spectral envelope in the resulting spectral estimates obtained the resolution properties of the periodogram estimate are preserved while the variance is reduced variance caused by the sidelobe leakage due to windows and additive noise are significantly reduced even in the spectral estimate obtained using a single realization of the observation peak resolution is primarily dictated by the size of the data window the method works even for high noise levels the results of this procedure are demonstrated through two illustrative examples estimation of sinusoids in noise and estimation of the narrowband autoregressive process in noise

an algorithm for pole zero modeling and spectral analysis an explicit connection between fitting exponential models and pole zero models to observed data is made the fitting problem is formulated as a constrained nonlinear minimization problem this problem is then solved using a simplified iterative algorithm the algorithm is applied to simulated data and the performance of the algorithm is compared to previous results

correlations in the bond future market we analyze the time series of overnight returns for the bund and btp futures exchanged at liffe london the overnight returns of both assets are mapped onto a one dimensional symbolic dynamics random walk the bond walk during the considered period october 1991 january 1994 the bund future market opened earlier than the btp future one the crosscorrelations between the two bond walks as well as estimates of the conditional probability show that they are not independent however each walk can be modeled by means of a trinomial probability distribution monte carlo simulations confirm that it is necessary to take into account the bivariate dependence in order to properly reproduce the statistical properties of the real world data various investment strategies have been devised to exploit the prior information obtained by the aforementioned analysis

a fast efficient technique for the estimation of frequency summary a technique is presented for the estimation of the frequency of a sinusoid in the presence of noise the technique is based on fitting arma 2 2 models iteratively in a special way the estimator is shown to be strongly consistent and as efficient as the least squares estimator of the frequency or the periodogram maximizer a simple accelerated version of the technique is shown to converge in a small number of iterations the number depending on the accuracy of the initiator of the procedure the results of a number of simulations are reported

an efficient approach for two dimensional parameter estimation of a single tone in this paper parameter estimation of a two dimensional 2 d single damped real complex tone in the presence of additive white gaussian noise is addressed by utilizing the rank one property of the 2 d noise free data matrix the damping factor and frequency for each dimension are estimated in a separable manner from the principal left and right singular vectors according to an iterative weighted least squares procedure the remaining parameters are then obtained straightforwardly using standard least squares the biases as well as variances of the damping factor and frequency estimates are also derived which show that they are approximately unbiased and their performance achieves crame r rao lower bound crlb at sufficiently large signal to noise ratio snr and or data size conditions we refer the proposed approach to as principal singular vector utilization for modal analysis puma which performs estimation in a fast and accurate manner the development and analysis can easily be adapted for a tone which is undamped in at least one dimension furthermore comparative simulation results with several conventional 2 d estimators and crlb are included to corroborate the theoretical development of the puma approach as well as to demonstrate its superiority

directed enumeration method in image recognition the article is devoted to the problem of image recognition in real time applications with a large database containing hundreds of classes the directed enumeration method as an alternative to exhaustive search is examined this method has two advantages first it could be applied with measures of similarity which do not satisfy metric properties chi square distance kullback leibler information discrimination etc second the directed enumeration method increases recognition speed even in the most difficult cases which seem to be very important in practical terms in these cases many neighbors are located at very similar distances in this paper we present the results of an experimental study of the directed enumeration method with comparison of color and gradient orientation histograms in solving the problem of face recognition with well known datasets essex feret it is shown that the proposed method is characterized by increased computing efficiency of automatic image recognition 3 12 times in comparison with a conventional nearest neighbor classifier

a lyapunov approach for robust nonlinear state feedback synthesis the authors apply the input output linearization approach for nonlinear state feedback synthesis the model uncertainty under consideration is a class of state model perturbations that satisfy appropriate matching conditions the controller design uses a lyapunov based approach to guarantee uniform ultimate boundedness of the states and the output

high spectral resolution time frequency distribution kernels a new class of time frequency distribution tfd kernels is introduced members in this class satisfy the desirable tfd properties and simultaneously provide local autocorrelation functions laf that are amenable to high frequency resolution modeling techniques it is shown that members of the proposed class are product kernels fast implementation multiplication free kernels recursive kernels and optimum kernels with respect to autoterm localization

on the family of ml spectral estimates for mixed spectrum identification a recently developed point spectrum identification procedure based on a family of ar and ml spectral estimates is exploited to arrive at a mixed spectrum identification procedure to this end a variety of properties of the ar and ml estimates as a function of model order are described these properties relate to amplitude convergence resolution and a characterization of the ar spectral artifact which is used to arrive at improved continuous spectral estimates a variety of examples are presented

bounds for estimation of complex exponentials in unknown colored noise we consider the problem of estimating the parameters of complex exponentials in the presence of complex additive gaussian noise with unknown covariance bounds are derived for the accuracy of jointly estimating the parameters of the exponentials and the noise we first present an exact cramer rao bound crb for this problem and specialize it for the cases of circular gaussian processes and autoregressive processes we also derive an approximate expression for the crb which is related to the conditional likelihood function numerical evaluation of these bounds provides some insights on the effect of various signal and noise parameters on the achievable estimation accuracy

scaling in the market of futures the price time series of the italian government bonds btp futures is studied by means of scaling concepts originally developed for random walks in statistical physics the series of overnight price differences is mapped onto a one dimensional random walk the bond walk the analysis of the root mean square fluctuation function and of the auto correlation function indicates the absence of both short and long range correlations in the bond walk a simple monte carlo simulation of a random walk with trinomial probability distribution is able to reproduce the main features of the bond walk

on spectral design methods for quasi cyclic codes a method is provided for constructing upper triangular square matrices over the univariate polynomial ring over a finite field under certain constraints on the eigenvalues of the matrices in some cases of interest the degree of the determinant of such matrices is shown to be the smallest possible the method is then applied to construct generator polynomial matrices of quasi cyclic codes for correcting phased burst errors finally an interpolation based list decoding algorithm is presented for these codes which for a wide range of code parameters is shown to outperform existing list decoding schemes

fast nearly ml estimation of the parameters of real or complex single tones or resolved multiple tones this paper presents new computationally efficient algorithms for estimating the parameters frequency amplitude and phase of one or more real tones sinusoids or complex tones cisoids in noise from a block of n uniformly spaced samples the first algorithm is an interpolator that uses the peak sample in the discrete fourier spectrum dfs of the data and its two neighbors we derive cramer rao bounds crbs for such interpolators and show that they are very close to the crb s for the maximum likelihood ml estimator the new algorithm almost reaches these bounds a second algorithm uses the five dfs samples centered on the peak to produce estimates even closer to ml enhancements are presented that maintain nearly ml performance for small values of n for multiple complex tones with frequency separations of at least 4 spl pi n rad sample unbiased estimates are obtained by incorporating the new single tone estimators into an iterative cyclic descent algorithm which is a computationally cheap nonlinear optimization single or multiple real tones are handled in the same way the new algorithms are immune to nonzero mean signals and provided n is large remain near optimal in colored and non gaussian noise

subspace approach for fast and accurate single tone frequency estimation a new signal subspace approach for estimating the frequency of a single complex tone in additive white noise is proposed in this correspondence our main ideas are to use a matrix without repeated elements to represent the observed signal and exploit the principal singular vectors of this matrix for frequency estimation it is proved that for small error conditions the frequency estimate is approximately unbiased and its variance is equal to cramer rao lower bound computer simulations are included to compare the proposed approach with the generalized weighted linear predictor periodogram and phase based maximum likelihood estimators in terms of estimation accuracy computational complexity and threshold performance

a fast and accurate single frequency estimator a novel frequency estimator for a single complex sinusoid in complex white gaussian noise is proposed the estimator is more computationally efficient that the optimal maximum likelihood estimator yet attains equally good performance at moderately high signal to noise ratios the estimator is shown to be related to the linear prediction estimator this relationship is used to reveal why the linear prediction estimator does not attain the cramer rao bound even at high signal to noise ratios

a recursive scheme for frequency estimation using the modulating functions method in this paper an application of modulating functions method for estimation of the frequency of noisy sinusoids is proposed the unknown frequency is updated by introducing a recursive algorithm which is independent by the choice of the modulating functions type the proposed recursive estimation formula is able to take into account possible abrupt changes or sweep in the frequency of the sinusoidal signal the goodness of the proposed method is verified through numerical simulations

parametric modeling for damped sinusoids from multiple channels the problem of parametric modeling for noisy damped sinusoidal signals from multiple channels is addressed utilizing the shift invariance property of the signal subspace the number of distinct sinusoidal poles in the multiple channels is first determined with the estimated number the distinct frequencies and damping factors are then computed with the multi channel weighted linear prediction method the estimated sinusoidal poles are then matched to each channel according to the extreme value theory of distribution of random fields simulations are performed to show the performance advantages of the proposed multi channel sinusoidal modeling methodology compared with existing methods

point process model of 1 f noise vs a sum of lorentzians we present a simple point process model of 1 f noise covering different values of the exponent the signal of the model consists of pulses or events the interpulse interevent interarrival recurrence or waiting times of the signal are described by the general langevin equation with the multiplicative noise and stochastically diffuse in some interval resulting in a power law distribution our model is free from the requirement of a wide distribution of relaxation times and from the power law forms of the pulses it contains only one relaxation rate and yields 1 f spectra in a wide range of frequencies we obtain explicit expressions for the power spectra and present numerical illustrations of the model further we analyze the relation of the point process model of 1 f noise with the bernamont surdin mcwhorter model representing the signals as a sum of the uncorrelated components we show that the point process model is complementary to the model based on the sum of signals with a wide range distribution of the relaxation times in contrast to the gaussian distribution of the signal intensity of the sum of the uncorrelated components the point process exhibits asymptotically a power law distribution of the signal intensity the developed multiplicative point process model of 1 f noise may be used for modeling and analysis of stochastic processes in different systems with the power law distribution of the intensity of pulsing signals

on the linear equivalents of nonlinear systems this paper studies feedback equivalence of nonlinear systems an equivalence relation is established for the systems x f x u a complete characterization of the class of nonlinear systems which are equivalent to controllable linear systems is obtained

global transformations of nonlinear systems recent results have established necessary and sufficient conditions for a nonlinear system of the form dot x t f x t u t g x t with f 0 0 to be locally equivalent in a neighborhood of the origin in rnto a controllable linear system we combine these results with several versions of the global inverse function theorem to prove sufficient conditions for the transformation of a nonlinear system to a linear system in doing so we introduce a technique for constructing a transformation under the assumptions that g ldot f dotg ad n 1 f ldotg span an n dimensional space and that g ldot f ldot g ad n 2 f ldotg is an involutive set

comparative study of iqml and mode direction of arrival estimators we present a comparative study of using the iterative quadratic maximum likelihood iqml and the method of direction estimation mode for direction of arrival doa estimation of narrowband incident signals with a uniform linear array the conditions for local convergence and the theoretical performance of iqml are derived although the doa estimates obtained via mode are asymptotically statistically efficient we show that those obtained via iqml are almost always inconsistent and have much larger mean squared errors we also show that the amount of computations required by iqml is usually much larger than that required by mode especially for low signal to noise ratio snr and a large number of snapshots

estimating the frequency of a noisy sinusoid by linear regression corresp the estimation of the parameters of a sinusoid from observations of signal samples corrupted by additive noise is investigated at high signal to noise ratios the additive noise is viewed as an equivalent phase noise suggesting frequency and phase estimation by linear regression on the signal phase the variances of the regression estimates are shown to achieve the cramer rao bounds a formula for the variance of the regression frequency estimator is derived in terms of the noise power spectrum a simple formula for the variance with 1 f 2 phase noise is presented

an adaptive correlator receiver for direct sequence spread spectrum communication this paper presents a novel receiver for direct sequence spread spectrum signals over channels containing interference and multipath the receiver employs an adaptive correlator that jointly detects the transmitted data removes interference and compensates for multipath the optimum correlation vector is derived by determining the wiener vector that minimizes the mean squared error mse between the transmitted data bit and the correlator output for an additive white gaussian noise awgn channel the optimal correlation vector is the spreading sequence used by the transmitter for interference and multipath channels the optimal correlation vector will suppress the interference and combine the multipath while optimizing the detection of the transmitted data bit the paper presents analytical and simulation results which illustrate the bit error rate ber performance of the receiver in multipath and narrowband interference additionally simulation results are presented illustrating the convergence performance of the receiver when the tap weights are adjusted using either the least mean square lms or recursive least squares rls adaptive algorithms

further results in the fast estimation of a single frequency the author proposes a new frequency estimator for a single complex sinusoid in complex white gaussian noise the estimator is applicable to problems in communications requiring high speed recursive frequency estimation the estimator is computationally efficient yet obtains near optimum performance at moderate signal to noise ratios

proper orthogonal decomposition and its applications part i theory abstract in view of the increasing popularity of the application of proper orthogonal decomposition pod methods in engineering fields and the loose description of connections among the pod methods the purpose of this paper is to give a summary of the pod methods and to show the connections among these methods firstly the derivation and the performance of the three pod methods karhunen loeve decomposition kld principal component analysis pca and singular value decomposition svd are summarized then the equivalence problem is discussed via a theoretical comparison among the three methods the equivalence of the matrices for processing the objective functions the optimal basis vectors the mean square errors and the asymptotic connections of the three methods are demonstrated and proved when the methods are used to handle the pod of discrete random vectors

estimation of the parameters of an arma signal model based on an orthogonal search an iterative procedure for estimating the parameters of an arma signal model input not accessible is empirically compared to the results of several other algorithms the arma identifier reported includes inherent order estimation the empirical results indicate unbiased estimates with less variance than those obtained by the other methods

exact equivalence of the steiglitz mcbride iteration and iqml the exact equivalence of the steiglitz mcbride mode 1 algorithm and the iterative quadratic maximum likelihood algorithm iqml is demonstrated other methods published earlier by l l scharf et al 1986 and by a g evans and r fischl 1973 are also known to give the same equations and answer as iqml hence all these methods are equivalent to the steiglitz mcbride iteration since the iqml method was originally formulated to solve a multiple measurement case an extended version of the steiglitz mcbride mode 1 algorithm applicable to the multiple measurement case is also stated and shown to be equivalent to the general form of iqml

robust control of non linear affine systems issue of robust control for non linear affine systems with uncertainties appearing in both drift and non drift terms are presented based on lyapunov function approach control laws are proposed to guarantee uniformly asymptotic stability of the equilibrium point to facilitate the design and simplify the checking procedure stabilization control for the uncertain systems possess asymptotic stabilizable nominal time invariant driftness terms are proposed for the demonstration of robust design

unitary puma algorithm for estimating the frequency of a complex sinusoid one dimensional 1 d and two dimensional 2 d frequency estimation for a single complex sinusoid in white gaussian noise is a classic signal processing problem with numerous applications it is revisited here through a new unitary principal singular vector utilization modal analysis puma approach which is realized in terms of real valued computations the 2 d unitary puma is first formulated as an iteratively weighted least squares optimization problem recognizing that only one iteration is sufficient when 2 d unitary puma is initialized using least squares a computationally attractive closed form solution is then obtained a variant of 2 d unitary puma is also developed for the 1 d case due to the real valued computations and closed form expression for the frequency estimate the unitary puma is more computationally efficient than a number of state of the art methods furthermore the asymptotic variances of 1 d and 2 d unitary puma estimators are theoretically derived and numerical results are included to demonstrate the effectiveness of the proposed methods

data based robust adaptive control for a class of unknown nonlinear constrained input systems via integral reinforcement learning this paper presents a data based robust adaptive control methodology for a class of nonlinear constrained input systems with completely unknown dynamics by introducing a value function for the nominal system the robust control problem is transformed into a constrained optimal control problem due to the unavailability of system dynamics a data based integral reinforcement learning rl algorithm is developed to solve the constrained optimal control problem based on the present algorithm the value function and the control policy can be updated simultaneously using only system data the convergence of the developed algorithm is proved via an established equivalence relationship to implement the integral rl algorithm an actor neural network nn and a critic nn are separately utilized to approximate the control policy and the value function and the least squares method is employed to estimate the unknown parameters by using lyapunov s direct method the obtained approximate optimal control is verified to guarantee the unknown nonlinear system to be stable in the sense of uniform ultimate boundedness two examples are provided to demonstrate the effectiveness and applicability of the theoretical results

adaptive robust stabilisation of uncertain nonlinear dynamical systems an improved backstepping approach abstractthe main purpose of this paper is to propose a design approach by which some simple adaptive robust controllers can be synthesised for a class of uncertain nonlinear dynamical systems which can be transformed into uncertain strict feedback nonlinear systems in this paper an improved backstepping design approach is presented to synthesising a class of continuous adaptive robust state feedback controllers with a rather simple structure the improved backstepping design approach can avoid the repeated differentiation problem which appears in using the conventional backstepping algorithm in particular it is not required to know the nonlinear upper bound functions of uncertainties in the light of the presented approach the state feedback controllers can be constructed to be linear in the state with the time varying control gains which can be self tuned by the adaptive laws similar to the conventional backstepping algorithm the improved backstepping approach can be extended to a rather large cl

parameters estimation of complex multitone signal in the dft domain in a previous paper a method was introduced that used a system of linear equations to determine the frequency and the complex amplitude of a complex single tone signal the system of linear equations was derived from the model of a complex single tone in the dft domain and was used as parameters estimator in noisy conditions in this paper this approach is extended to a complex multitone signal as for the complex single tone case the parameters value is determine from the solution of systems of linear equations and the method is directly applied for parameters estimation in noisy conditions an analysis of the estimators is provided including the case of closely spaced frequencies and criteria and procedures for the appropriate selection of the dft samples are presented