sensitivity analysis for antenna near field imaging although imaging and inverse scattering problems have been thoroughly studied during the last century there is only a partial understanding of these complex problems most of the efforts have been placed on the development of efficient inversion algorithms and mathematical uniqueness results in comparison there are very few results and a limited knowledge about the information content in the inversion data in this paper we provide a mathematical framework for sensitivity analysis of antenna near field imaging problems based on the multipole expansion of the electromagnetic field and the fisher information to quantify the quality of data by exploiting this framework a fundamental relation for accuracy and resolution is formulated based on the crameacuter rao bound crb the sensitivity analysis is illustrated using a relevant example with cylindrical measurement data

the beta model maximum likelihood cramer rao bounds and hypothesis testing we study the maximum likelihood estimator in a setting where the dependent variable is a random graph and covariates are available on a graph level the model generalizes the well known beta model for random graphs by replacing the constant model parameters with regression functions cramer rao bounds are derived for special cases of the undirected beta model the directed beta model and the covariate based beta model the corresponding maximum likelihood estimators are compared with the bounds by means of simulations moreover examples are given on how to use the presented maximum likelihood estimators to test for directionality and significance finally the applicability of the model is demonstrated using temporal social network data describing communication among healthcare workers

joint estimation of porosity and saturation using stochastic rock physics modeling sediment porosity and saturation affect bulk modulus shearmodulus anddensity consequently estimatinghydrocarbon saturation and reservoir porosity from seismic data is a joint estimation problem uncertainty in porosity will lead toerrorsinsaturationprediction andviceversa porosityand saturation can be jointly estimated using stochastic rockphysicsmodelingandformalbayesianestimationmethodology knowledge of shear impedance reduces the uncertainty inporosityandthusalsoreducesuncertaintyinsaturationestimation this study investigates joint estimation of porosity and saturation by using rock physics stochastic modeling and bayesian estimation theory to derive saturation and porosity maps of expected pay sands in the field example the uncertainty in porosity quantified by the standard deviation stdassociatedwiththeposteriorprobabilitydensityfunctionpdf derivedfrominversionofseismicdataismuchless than the uncertainty in the derived saturation for a typical case the std associated with saturation is 24 while porositystdisabout1 34porosityunitsgivenseismic derived inversionattributeswithreasonableaccuracy comparisonof these numbers with prior estimates showed that inversion of seismic data decreased the uncertainty in porosity to 15 of the prior uncertainty while saturation uncertainty was only reduced to 92 of the prior uncertainty although these resultsmayvaryfromonelocationtoanother themethodology isgeneralandcanbeappliedtootherlocations

a simple precoding based blind channel estimation for space time block coded single carrier with frequency domain equalization systems we propose a blind channel estimation method for space time block coded single carrier with frequency domain equalization sc fde systems based on a simple nonredundant precoding this method of precoding allows us to better structure the relation between the covariance matrices of the received data and the products of channel coefficients using this structure we can easily obtain the products of channel coefficients by using only subtraction and division then each of the two channel impulse response vectors is obtained by computing the eigenvector that is associated with the maximal eigenvalue of a hermitian matrix formed from the corresponding products of channel coefficients the proposed framework guarantees the unique recovery of all the products of channel coefficients for colored or white noise cases the impact of the precoding on channel estimation and equalization is also discussed simulations are used to demonstrate the performance of the proposed method

space time block codes from orthogonal designs we introduce space time block coding a new paradigm for communication over rayleigh fading channels using multiple transmit antennas data is encoded using a space time block code and the encoded data is split into n streams which are simultaneously transmitted using n transmit antennas the received signal at each receive antenna is a linear superposition of the n transmitted signals perturbed by noise maximum likelihood decoding is achieved in a simple way through decoupling of the signals transmitted from different antennas rather than joint detection this uses the orthogonal structure of the space time block code and gives a maximum likelihood decoding algorithm which is based only on linear processing at the receiver space time block codes are designed to achieve the maximum diversity order for a given number of transmit and receive antennas subject to the constraint of having a simple decoding algorithm the classical mathematical framework of orthogonal designs is applied to construct space time block codes it is shown that space time block codes constructed in this way only exist for few sporadic values of n subsequently a generalization of orthogonal designs is shown to provide space time block codes for both real and complex constellations for any number of transmit antennas these codes achieve the maximum possible transmission rate for any number of transmit antennas using any arbitrary real constellation such as pam for an arbitrary complex constellation such as psk and qam space time block codes are designed that achieve 1 2 of the maximum possible transmission rate for any number of transmit antennas for the specific cases of two three and four transmit antennas space time block codes are designed that achieve respectively all 3 4 and 3 4 of maximum possible transmission rate using arbitrary complex constellations the best tradeoff between the decoding delay and the number of transmit antennas is also computed and it is shown that many of the codes presented here are optimal in this sense as well

limits of sensing temporal concentration changes by single cells berg and purcell biophys j 20 193 1977 calculated how the accuracy of concentration sensing by single celled organisms is limited by noise from the small number of counted molecules here we generalize their results to the sensing of concentration ramps which is often the biologically relevant situation e g during bacterial chemotaxis we calculate lower bounds on the uncertainty of ramp sensing by three measurement devices a single receptor an absorbing sphere and a monitoring sphere we contrast two strategies simple linear regression of the input signal versus maximum likelihood estimation and show that the latter can be twice as accurate as the former finally we consider biological implementations of these two strategies and identify possible signatures that maximum likelihood estimation is implemented by real biological systems

a governing equation for fluid flow in rough fractures fluid flow in a rock fracture bounded by two irregular surfaces is complex even under a laminar flow regime the major factor causing deviation of predicted fracture flow behavior from the ideal parallel plate theory is the nature of nonparallel and nonsmooth geometry of fracture surfaces important questions on the validity of the cubic law and the reynolds equation for complicated fracture geometries have been studied by many researchers the general conclusion from these efforts is that the cubic law is valid provided that an appropriate average aperture can be defined many average apertures have been proposed and for some cases some work better than others nonetheless to date these efforts have not converged to form a unified definition on the fracture aperture needed in the cubic law which stimulates the current effort to develop a general governing equation for fracture flow from a fundamental consideration in this study a governing equation stemming from the principle of mass conservation and the assumption that the cubic law holds locally is derived for incompressible laminar fluid flow in irregular fractures under steady state conditions the equation is formulated in both local and global coordinates and explicitly incorporates two vectorial variables of fracture geometry true aperture and tortuosity under the assumption of small variations in both tortuosity and aperture the governing equation can be reduced to the reynolds equation two examples are provided to show the importance and generality of the new governing equation in both local and global coordinate systems in a simple fracture with two nonsmooth and nonparallel surfaces the error in permeability estimation can be induced using the reynolds equation with the apparent aperture and can reach 10 for a 25 inclination between the fracture surfaces in a fracture with sinusoidal surfaces the traditional method can cause significant errors in both permeability and pressure calculation

robust linear channel estimation methods for per subcarrier transmit antenna selection the linear minimum mean squared error mse channel estimator for systems employing per subcarrier transmit antenna selection is developed frequency domain correlations after the selection process are shown to be approximated well using a simple function which makes near optimal channel estimation practically possible however the resultant estimators are not robust to errors in the assumed model in terms of the antenna to subcarrier assignment used at transmission as we motivate both analytically and via simulations this is an issue when the channels for conveying this information to the receiver are severely constrained we present windowed channel estimations and new robust estimation algorithms either individually or in combination as solutions to counter this sensitivity the robust estimators are developed based on the principles of 1 minimising the worst case mse over the set of possible models and 2 minimising the expected mse over the set of possible models the latter estimator is preferred due to the lower implementation complexity and better mse performance we also prove theoretical asymptotic in signal to noise ratio performances of the two estimators used with the proposed correlation model simulation results illustrate the performance gains and improved robustness offered by the developed estimators

active learning applied to rcs computations with nonuniform sampling using different objective functions an active learning framework is introduced to reduce the number of frequencies and angles one must consider for wideband monostatic scattering computations or measurements this method is used to optimally select those frequencies and angles that would be most informative resulting in nonuniform sampling and often a reduced number of points vis agrave vis uniform sampling in this paper we focus on jointly two dimensional optimal sampling in frequency and incident angle thetas for monostatic scattering the method consists of two basic steps one step involves estimation of model parameters using a least square ls algorithm the next step is to optimally choose the next point frequency and thetas for analysis by the computational model or experiment this new point is selected with the goal of reducing uncertainty in the parametric model quantified via the fisher information matrix iterating these two steps a sequence of numerical computations or measurements are performed each at the most informative point for learning the parameters of the associated simpler parametric model this idea is demonstrated here in the context of reducing the number of points frequencies and orientations at which a computational model must be employed and in order to avoid repeatedly gathering samplings at the edge of the input space an alternative objective function is applied which makes the actively selected points closer to a region of interest

plots and tests for goodness of fit with randomly censored data summary in this paper we consider nonparametric procedures for assessing goodness of fit when the data may be subject to random censoring we introduce two classes of large sample tests based respectively on the maximum and average weighted difference between the specified survival function and the kaplan meier estimate of the true survival function the procedures in the first class are inverted to obtain simultaneous confidence bands for the survival and cumulative hazard functions these bands provide simultaneous bands associated with the percentage p p quantile q q and hazard plots for censored data some key word8 hazard function nonparametric procedure probability plot shift function survival function

convergence rates for the bootstrapped product limit process on etablit des taux d approximation forte pour le processus limite produit du bootstrap et le processus quantile correspondant

an elementary approach to weak convergence for quantile processes with applications to censored survival data abstract let be a continuously differentiable function with positive derivative and let n be a sequence of right continuous increasing processes we show that if n 1 2 n w where w is continuous then n 1 2 1 n 1 w 1 1 this result is applied to classical processes such as the empirical distribution function the kaplan meier estimator and some other situations we also prove an analogous result for the bootstrapped version of n 1 2 n and show how this allows one to obtain confidence bands for the quantile function 1 based on the bootstrap several examples are given

some asymptotic theory for the bootstrap efron s bootstrap method of distribution approximation is shown to be asymptotically valid in a large number of situations including t statistics the empirical and quantile processes and von mises functionals some counter examples are also given to show that the approximation does not always succeed

hydraulic properties of two dimensional random fracture networks following a power law length distribution 2 permeability of networks based on lognormal distribution of apertures the broad length and aperture distributions are two characteristics of the heterogeneity of fractured media that make difficult and even theoretically irrelevant the application of homogenization techniques we propose a numerical and theoretical study of the consequences of these two properties on the permeability of bidimensional synthetic fracture networks we use a power law for the model of length distribution and a lognormal model for aperture distribution we have especially studied the two endmost models for which length and aperture are 1 independent and 2 perfectly positively correlated for the model without correlation between length and aperture we show that the permeability can be adequately characterized by a power averaging function whose parameters are detailed in the text in contrast for the model with correlation we show that the prevailing parameter is the correlation when the power law length exponent a is lower than 3 whereas the random structure of the network is a second order parameter we also determine the permeability scaling and the scale dependence of the flow pattern structure three types of scale effects are found depending exclusively on the geometrical properties of the network i e on the length distribution parameter a for a larger than 3 permeability decreases for scales below a definite correlation length and becomes constant above we show in this case that a correlation between length and aperture does not fundamentally change the permeability model in all other cases the correlation entails much larger scale effects for a in the range 1 3 in the case of an absence of correlation and for a in the range 2 3 in the case of correlation permeability increases and tends to a limit whereas the flow structure is channeled when permeability increases and tends to homogenize when permeability tends to its limit we note that this permeability model is consistent with natural observations of permeability scaling for a in the range 1 2 in the case of correlation permeability increases with scale with no apparent limit we characterize the channeled flow pattern and we show that permeability may increase even when flow is distributed in several independent structures

calculation of the fisher information matrix for multidimensional data sets data sets that are acquired in many practical systems can be described as the output of a multidimensional linear separable denominator system with gaussian measurement noise an important example is nuclear magnetic resonance nmr spectroscopy in nmr spectroscopy high accuracy parameter estimation is of central importance a classical result on the crame spl acute r rao lower bound states that the inverse of the fisher information matrix fim provides a lower bound for the covariance of any unbiased estimator of the parameter vector the calculation of the fim is therefore of central importance for an assessment of the accuracy with which parameters can be estimated it is shown how the fim can be expressed using the matrices that determine the system that generates the data set for uniformly sampled data it is shown how the fim can be expressed through the solutions of lyapunov equations the novel techniques are demonstrated with an example arising from nmr spectroscopy

strong approximations of some biometric estimates under random censorship the empirical distribution function has been widely used as an estimator for the distribution function of the elements of a random sample it is not however appropriate when the observations are incomplete this is the case for example when the observations have been randomly censored on the right in the latter case the so called product limit estimate of kaplan and meier 16 has been generally accepted as a substitute for the empirical distribution function and it possesses many of the same properties cf meier 19 developing the corresponding statement of efron 8 weak convergence over a finite interval of the kaplan meier product limit estimate to a gaussian process when there is censorship on the right has been obtained by breslow and crowley 3 cf also meier 19 breslow and crowley obtained their result via the limiting distribution of the closely related cumulative hazard process under random censorship a generalisation of the efron breslow crowley theorem was formulated by yang 24 for the case when censorship on the right is performed by more competing risks these results have numerous statistical applications in areas such as medical follow up studies life testing actuarial sciences and demography koziol and green 18 have computed tables for the limiting distribution of the cram6r von mises statistic based on the kaplan meier product limit estimate under a specific random censorship model a point will be stressed in this paper however that by a certain transformation which seems to have escaped many researchers attention and is due to efron 8 it is always possible to obtain the standard wiener process in the limit the distributions of many functionals of which are well known this transformation was only recently used in a stimulating note by gillespie and fisher 14 to construct asymptotic confidence bands for the unknown distribution function in the kaplan meier model a general scheme of random censorship can be defined in the following way let x be a real random variable with distribution function f t

retinal vessel centerline extraction using multiscale matched filters confidence and edge measures motivated by the goals of improving detection of low contrast and narrow vessels and eliminating false detections at nonvascular structures a new technique is presented for extracting vessels in retinal images the core of the technique is a new likelihood ratio test that combines matched filter responses confidence measures and vessel boundary measures matched filter responses are derived in scale space to extract vessels of widely varying widths a vessel confidence measure is defined as a projection of a vector formed from a normalized pixel neighborhood onto a normalized ideal vessel profile vessel boundary measures and associated confidences are computed at potential vessel boundaries combined these responses form a six dimensional measurement vector at each pixel a training technique is used to develop a mapping of this vector to a likelihood ratio that measures the vesselness at each pixel results comparing this vesselness measure to matched filters alone and to measures based on the hessian of intensities show substantial improvements both qualitatively and quantitatively the hessian can be used in place of the matched filter to obtain similar but less substantial improvements or to steer the matched filter by preselecting kernel orientations finally the new vesselness likelihood ratio is embedded into a vessel tracing framework resulting in an efficient and effective vessel centerline extraction algorithm

nonredundant precoding assisted blind channel estimation for single carrier space time block coded transmission with frequency domain equalization relying on nonredundant diagonal precoding and independent and identically distributed i i d source assumption this paper proposes a blind channel estimation scheme for single carrier frequency domain equalization based space time block coded systems the proposed method exploits the precoding induced linear signal structure in the conjugate cross correlation between the two temporal block received signals as well as the circulant channel matrix property and can yield exact solutions whenever the channel noise is circularly gaussian and the receive data statistic is perfectly obtained the channel estimation formulation builds on rearranging the set of linear equations relating the entries of conjugate cross correlation matrix and products of channel impulse responses into one with a distinctive block circulant with circulant block bccb structure this allows a simple identifiability condition depending on precoder parameters alone and also provides a natural yet effective optimal precoder design framework for improving solution accuracy when imperfect data estimation occurs we consider two models of data mismatch from both deterministic and statistical points of view and propose the associated design criteria the optimization problems are formulated to take advantage of the bccb system matrix property and are solved analytically the proposed optimal precoder aims to optimize solution robustness against deterministic error perturbation and also minimize the mean square error when the data mismatch is modeled as a white noise pairwise error probability analysis is conducted for investigating the equalization performance numerical examples are used to illustrate the performance of the proposed method

iterative cdma multiuser receiver with soft decision directed channel estimation we develop an iterative code division multiple access cdma receiver for multipath block fading channels in the uplink the mean square error mse of the channel estimates is reduced iteratively by using soft data estimates as additional pilots the multiuser detector performs parallel interference cancellation pic with subsequent linear filtering we consider a single user matched filter sumf and develop a low complexity linear minimum mse lmmse filter for intersymbol interference isi channels that allows for 33 more users than the sumf we investigate two choices of training sequences for initial channel estimation the first is a random pilot symbol sequence modulated by a short spreading sequence rand and the second is a perfect root of unity sequence prus used as long sounding chip sequence we observe that the rand scheme approaches the single user bound up to 2 db and in case of prus up to 1 db at a target bit error rate ber of 10 3 further several channel estimators are developed that use various combinations of soft decision symbols and statistical a priori knowledge about the channel these are the approximated least squares als approximated linear mmse almmse and linear mmse lmmse estimators the iterative estimators show an improvement of 2 5 and 5 db with prus and rand pilots respectively over estimators that do not exploit soft feedback symbols at a ber of 10 3 the als is sacrificing less than 0 6 db to obtain a ber of 10 3 in a system at load 1 5 when compared with the almmse and lmmse schemes the load is understood as the ratio of the number of users to the spreading factor this paper shows that significant improvements in system capacity can be achieved in multipath scenarios with low complexity choices of multiuser detectors and channel estimators

bootstrapping the kaplan meier estimator abstract randomly censored data consist of iid pairs of observations xi i i 1 n if i 0 xi denotes a censored observation and if i 1 xi denotes an exact survival time which is the variable of interest for estimating the distribution f of the survival times the product limit estimator proposed by kaplan and meier 1958 has been studied extensively and it has been shown to enjoy a number of optimality properties wellner 1982 see gill 1980 chaps 1 4 1983 for a modern treatment and for references with censored data bootstrapping can be carried out using two different resampling plans introduced by efron 1981 and reid 1981 respectively with efron s plan one takes a random sample with replacement from x 1 xn n whereas with reid s plan one takes a random sample from the kaplan meier estimator the purpose of this article is to study the asymptotic behavior of the bootstrapped kaplan meier estimator with both resampling plans the approach adopted uses the

em based recursive estimation of channel parameters recursive online expectation maximization em algorithm along with stochastic approximation is employed in this paper to estimate unknown time invariant variant parameters the impulse response of a linear system channel is modeled as an unknown deterministic vector process and as a gaussian vector process with unknown stochastic characteristics using these models which are embedded in white or colored gaussian noise different types of recursive least squares rls kalman filtering and smoothing and combined rls and kalman type algorithms are derived directly from the recursive em algorithm the estimation of unknown parameters also generates new recursive algorithms for situations such as additive colored noise modeled by an autoregressive process the recursive em algorithm is shown as a powerful tool which unifies the derivations of many adaptive estimation methods

bounds on maximum likelihood ratio part ii application to antenna array detection estimation with imperfect wavefront coherence the multiple hypothesis testing problem of the detection estimation of an unknown number of independent gaussian point sources is adequately addressed by likelihood ratio lr maximization over the set of admissible covariance matrix models we introduce nonasymptotic lower and upper bounds for the maximum lr since lr optimization is generally a nonconvex multiextremal problem any practical solution could now be tested against these bounds enabling a high probability of recognizing nonoptimal solutions we demonstrate that in many applications the lower bound is quite tight with approximate maximum likelihood ml techniques often unable to approach this bound the introduced lower bound analysis is shown to be very efficient in determining whether or not performance breakdown has occurred for subspace based direction of arrival doa estimation techniques we also demonstrate that by proper lr maximization we can extend the range of signal to noise ratio snr values and or number of data samples wherein accurate parameter estimates are produced yet when the snr and or sample size falls below a certain limit for a given scenario we show that ml estimation suffers from a discontinuity in the parameter estimates a phenomenon that cannot be eliminated within the ml paradigm

on the evaluation of the polyanskiy poor verdu converse bound for finite block length coding in awgn a tight converse bound to the channel coding rate in the finite block length regime and under additive white gaussian noise conditions was recently proposed by polyanskiy poor verdu ppv the bound is a generalization of a number of other classical results and it was also claimed to be equivalent to shannon s 1959 cone packing bound in this paper we investigate methods for a reliable numerical evaluation of the bound which is troublesome even for not too large values of the block length n by compactly expressing the polyanskiy poor and verdu ppv converse bound in terms of non central chi squared distributions and by evaluating those through an integral expression and a corresponding series expansion which exploit a method proposed by temme as a result a robust evaluation method and new insights on the bound s asymptotics as well as new approximate expressions are obtained

on minimum variance unbiased estimation of clock offset in a two way message exchange mechanism for many applications distributed networks require the local clocks of the constituent nodes to run close to an agreed upon notion of time most of the widely used clock synchronization algorithms in such systems employ the sender receiver protocol based on a two way timing message exchange paradigm maximum likelihood estimator mle of the clock offset based on the timing message exchanges between two clocks was derived in d r jeske on maximum likelihood estimation of clock offset ieee trans commun vol 53 pp 53 54 jan 2005 when the fixed delays are symmetric and the variable delays in each direction assume an exponential distribution with an unknown mean herein the best linear unbiased estimate using order statistics blue os of the clock offset between two nodes is derived assuming both symmetric and asymmetric exponential network delays respectively the rao blackwell lehmann scheffe theorem is then exploited to obtain the minimum variance unbiased estimate mvue for the clock offset which it is shown to coincide with the blue os in addition it is found that the mvue of the clock offset in the presence of symmetric network delays also coincides with the mle finally in the presence of asymmetric network delays although the mle is biased it is shown to achieve lesser mean square error mse than the mvue in the region around the point where the bidirectional network link delays are symmetric and hence its merit as the most versatile estimator is fairly justified

class of widely linear complex kalman filters recently a class of widely linear augmented complex valued kalman filters kfs that make use of augmented complex statistics have been proposed for sequential state space estimation of the generality of complex signals this was achieved in the context of neural network training and has allowed for a unified treatment of both second order circular and noncircular signals that is both those with rotation invariant and rotation dependent distributions in this paper we revisit the augmented complex kf augmented complex extended kf and augmented complex unscented kf in a more general context and analyze their performances for different degrees of noncircularity of input and the state and measurement noises for rigor a theoretical bound for the performance advantage of widely linear kfs over their strictly linear counterparts is provided the analysis also addresses the duality with bivariate real valued kfs together with several issues of implementation simulations using both synthetic and real world proper and improper signals support the analysis

representation of mutual information via input estimates a relationship between information theory and estimation theory was recently shown for the gaussian channel relating the derivative of mutual information with the minimum mean square error this paper generalizes the link between information theory and estimation theory to arbitrary channels giving representations of the derivative of mutual information as a function of the conditional marginal input distributions given the outputs we illustrate the use of this representation in the efficient numerical computation of the mutual information achieved by inputs such as specific codes or natural language

on non newtonian fluid flow in rough fractures flow of non newtonian fluids between rough walls is of interest in several geophysical and industrial applications in this work mainly geared toward fractured media a governing equation for creeping flow of a purely viscous power law fluid of flow behavior index n in a rough walled fracture is obtained generalizing past results for a newtonian fluid an equivalent fracture aperture is defined in analogy to the well known hydraulic aperture valid for n 1 tortuosity is introduced as a vectorial quantity thereby distinguishing between true and apparent fracture aperture examples are provided to illustrate the utility of the proposed approach it is demonstrated that tortuosity effects significantly decrease the equivalent fracture permeability depending on the specific geometry considered the flow behavior index may or may not have a significant impact on the equivalent fracture permeability when it does the reduction effect due to tortuosity is enhanced as the flow behavior index decreases

non redundant precoding based blind and semi blind channel estimation for mimo block transmission with a cyclic prefix by combining multiple input multiple output mimo communication with block transmission using a cyclic prefix cp mimo block transmission systems can not only achieve high data rates but also simplify channel estimation and equalization in this paper to provide a bandwidth efficient solution for channel estimation in mimo block transmission systems with a cp we present a framework for blind channel estimation based on a general non redundant precoding using this framework we propose a blind estimator exploiting a simplified non redundant precoding that is robust against overestimates of a true mimo channel order furthermore in the case with the number of transmit antennas greater than that of receive antennas we show under specific system conditions that the proposed blind algorithms can be used for estimation of the mimo channel without oversampling the received signals with the simplified precoding conditions established in this paper the proposed method does not impose the strict identifiability conditions on the mimo channel which are required for the existing methods furthermore the proposed estimator achieves accurate channel estimation with a small number of symbol blocks in addition we derive a simplified precoder that is optimized in the sense of minimizing the impact of unknown additive noise we discuss the tradeoff between channel estimation accuracy and bit error rate performance associated with the simplified precoder finally we develop a technique for resolving the channel ambiguity in the proposed blind estimator to create a semi blind channel estimator relying on only a few known symbols

maximum multipath diversity with linear equalization in precoded ofdm systems in wireless communications the fading multipath channel attenuates and distorts the transmitted signal to decode the transmitted symbols and take advantage of the full multipath diversity that the channel has to offer computationally complex maximum likelihood ml decoding is often employed we show that a linear equalizer followed by a hard decision is capable of benefiting from maximum multipath diversity in linearly precoded orthogonal frequency division multiplexing ofdm systems where the information symbols are mapped through a matrix transformation before the inverse fast fourier transform ifft at the ofdm transmitter as far as we are aware this is the first proof of a linear equalization scheme achieving maximum multipath diversity over single input single output wireless links we can conclude from this result that at sufficiently high signal to noise ratios snr precoded ofdm systems will perform better over channels with more taps even with linear equalization due to the increase in diversity order

improvement of tdoa position fixing using the likelihood curvature a conventional multilateration is a two step process where in the first step the time difference of arrivals tdoa of a signal at multiple sensors are estimated and in the second step these tdoas are used in some position fixing technique to estimate the location of an emitter several estimators have been proposed over the years for the estimation of the tdoas many techniques have been proposed for position fixing as well much of the research on position fixing has been focused on obtaining a simplified closed form solution for the unknown deterministic signal model stein had derived the maximum likelihood estimator mle for the tdoa between two sensors which is the peak location of the cross correlation function since the asymptotic variance of an mle approaches the cramer rao lower bound crlb which is the inverse of the negative of the expected value of the curvature of the log likelihood function using this as a motivation we propose a weighted least squares type position fixing technique where the weights are computed from the curvature of the log likelihood function

accurate bootstrap confidence limits for the cumulative hazard and survivor functions under random censoring abstract in clinical trials and other settings confidence intervals for survival probabilities at a fixed point in time are often required typically such intervals are constructed on the cumulative hazard scale the quantiles required for computing these intervals are generally obtained using a first order normal theory approximation the appropriateness of such intervals for small sample sizes or under moderate to heavy censoring is suspect we demonstrate that significant improvement is possible for randomly censored data using the nonparametric bootstrap in particular using edgeworth expansion it is shown that the percentile t and bc a bootstrap methodologies based on the studentized nelson aalen estimator yield second order correct confidence limits for the cumulative hazard function at a fixed time point an explicit formula for the correction term of the edgeworth expansion is found easy to compute estimators for the components of the correction term are also found and lead to simple analytic

the risk unbiased cramer rao bound for non bayesian multivariate parameter estimation how accurately can one estimate a deterministic parameter subject to other unknown deterministic model nuisance parameters the most popular answer to this question is given by the cramer rao bound crb the main assumption behind the derivation of the crb is local unbiased estimation of all model parameters the foundations of this paper rely on doubting this assumption generally in multivariate parameter estimation each parameter in its turn can be treated as a single parameter of interest whereas the other model parameters are treated as nuisance as their misknowledge interferes with the estimation of the parameter of interest this approach is utilized in this paper to provide a fresh look at deterministic parameter estimation a new cramer rao cr type bound is derived without assuming unbiased estimation of the nuisance parameters rather than that we apply lehmann s concept of unbiasedness for a risk that measures the distance between the estimator and the locally best unbiased estimator which assumes perfect knowledge of the nuisance parameters the proposed risk unbiased crb rucrb is proven to be asymptotically attainable by the maximum likelihood estimator while being tighter than the conventional crb furthermore simulations verify the asymptotic achievability of the rucrb by the maximum likelihood estimator for an array processing problem

the fracture flow equation and its perturbation solution 1 this work derives the fracture flow equation from the two dimensional steady form of the navier stokes equation asymptotic solutions are obtained whereby the perturbation parameter is the ratio of the mean width over the length of the fracture segment the perturbation expansion can handle arbitrary variation of the fracture walls as long as the dominant velocity is in the longitudinal direction the effect of the matrix fracture interaction is also taken into account by allowing leakage through the fracture walls the perturbation solution is used to obtain an estimate of the flow rate and the fracture transmissivity as well as the velocity and the pressure distribution in fractures of various geometries the analysis covers eight different configurations of fracture geometry including linear and curvilinear variation as well as sinusoidal variation in the top and bottom walls with varying horizontal alignment and roughness wavelengths the zero order solution yields the reynolds lubrication approximation and the higher order equations provide a correction term to the flow rate in terms of the roughness frequency and the reynolds number for sinusoidal and linear walls the mathematical analysis shows that the zero order flow rate could be expressed in terms of the maximum to minimum width ratio for equal widths at both ends of the fracture the first order correction is zero for sinusoidal fractures the flow rate decreases with increasing reynolds number and with increasing roughness amplitude and frequency the effect of leakage is to create a nonuniform flow distribution in the fracture that deviates significantly from the flow rate estimate for impermeable walls the derived flow expressions can provide a more reliable tool for flow and transport predictions in fractured domain

ber minimized ofdm systems with channel independent precoders we consider the minimization of uncoded bit error rate ber for the orthogonal frequency division multiplexing ofdm system with an orthogonal precoder we analyze the ber performance of precoded ofdm systems with zero forcing and minimum mean squared error mmse receivers in the case of mmse receivers we show that for quadrature phase shift keying qpsk there exists a class of optimal precoders that are channel independent examples of this class include the discrete fourier transform dft matrix and the hadamard matrix when the precoder is the dft matrix the resulting optimal transceiver becomes the single carrier system with cyclic prefix sc cp system we also show that the worst solution corresponds to the conventional ofdm system the conventional ofdm system has the largest ber in the case of zero forcing receivers the design of optimal transceiver depends on the signal to noise ratio snr for higher snr solutions of optimal precoders are the same as those of mmse receivers

on the koziol green model for random censorship summary in the koziol green 1976 model of random censorship the survival distribution of the censoring variables is some power the censoring parameter of the survival distribution of the lifetimes using a strong approximation result of burke csorgo and horvath we construct empirical exact confidence bands for the life distribution of the censored variable and a randomly efron transformed variant of the usual product limit process is shown to converge weakly to the brownian motion process on any interval 0 t when the censoring parameter is also estimated from the sample the technique automatically gives rates of convergence for a wide class of functionals including the cramer von mises functional whose limit theory is worked out in detail the latter goodness of fit statistic is applied to reexamine the hospital data of koziol green 1976 to test whether oestrogen treatment for prostatic cancer was effective or not for the cancer itself while it caused censoring deaths of cardiovascular diseases

bilinear approach to multiuser second order statistics based blind channel estimation we present a bilinear approach to multiple input multiple output mimo blind channel estimation where products of the channel parameters are first estimated from the covariance of the received data the channel parameters are then obtained as the dominant eigenvectors of the outer product estimate necessary and sufficient identifiability conditions are presented for a single channel and extended to the multichannel case it is found that this technique can identify the channel to within a subspace ambiguity as long as the basis functions for the channel satisfy certain constraints regardless of the left invertability of the channel matrix one important requirement for identifiability is that the number of channel parameters is small compared with the channel length advantageously this is exactly the situation in which this algorithm has significantly lower complexity than competing parametric multiuser blind algorithms simulations show that the technique is applicable in situations where typical identifiability conditions fail common nulls a single symbol spaced channel and more users than channels these simulations are for the almost flat faded situation when the propagation delay spread is a fraction of the transmission pulse duration as might be found in current tdma systems comparisons are made when possible to a subspace method incorporating knowledge of the basis functions the bilinear approach requires significantly less computation but performs better than the subspace method at low snr especially for multiple users

robust rls in the presence of correlated noise using outlier sparsity relative to batch alternatives the recursive least squares rls algorithm has well appreciated merits of reduced complexity and storage requirements for online processing of stationary signals and also for tracking slowly varying nonstationary signals however rls is challenged when in addition to noise outliers are also present in the data due to e g impulsive disturbances existing robust rls approaches are resilient to outliers but require the nominal noise to be white an assumption that may not hold in e g sensor networks where neighboring sensors are affected by correlated ambient noise prewhitening with the known noise covariance is not a viable option because it spreads the outliers to noncontaminated measurements which leads to inefficient utilization of the available data and unsatisfactory performance in this correspondence a robust rls algorithm is developed capable of handling outliers and correlated noise simultaneously in the proposed method outliers are treated as nuisance variables and estimated jointly with the wanted parameters identifiability is ensured by exploiting the sparsity of outliers which is effected via regularizing the least squares ls criterion with the l1 norm of the outlier vectors this leads to an optimization problem whose solution yields the robust rls estimates for low complexity real time operation a suboptimal online algorithm is proposed which entails closed form updates per time step in the spirit of rls simulations demonstrate the effectiveness and improved performance of the proposed method in comparison with the nonrobust rls and its state of the art robust renditions

an improved two dimensional depth integrated flow equation for rough walled fractures 1 we present the development of an improved 2 d flow equation for rough walled fractures our improved equation accounts for the influence of midsurface tortuosity and the fact that the aperture normal to the midsurface is in general smaller than the vertical aperture it thus improves upon the well known reynolds equation that is widely used for modeling flow in fractures unlike the reynolds equation our approach begins from the lubrication approximation applied in an inclined local coordinate system tangential to the fracture midsurface the local flow equation thus obtained is rigorously transformed to an arbitrary global cartesian coordinate system invoking the concepts of covariant and contravariant transformations for vectors defined on surfaces unlike previously proposed improvements to the reynolds equation our improved flow equation accounts for tortuosity both along and perpendicular to a flow path our approach also leads to a well defined anisotropic local transmissivity tensor relating the representations of the flux and head gradient vectors in a global cartesian coordinate system we show that the principal components of the transmissivity tensor and the orientation of its principal axes depend on the directional local midsurface slopes in rough walled fractures the orientations of the principal axes of the local transmissivity tensor will vary from point to point the local transmissivity tensor also incorporates the influence of the local normal aperture which is uniquely defined at each point in the fracture our improved flow equation is a rigorous statement of mass conservation in any global cartesian coordinate system we present three examples of simple geometries to compare our flow equation to analytical solutions obtained using the exact stokes equations an inclined parallel plate and circumferential and axial flows in an incomplete annulus the effective transmissivities predicted by our flow equation agree very well with values obtained using the exact stokes equations in all these cases we discuss potential limitations of our depth integrated equation which include the neglect of convergence divergence and the inaccuracies implicit in any depth averaging process near sharp corners where the wall and midsurface curvatures are large

robust precoding with limited feedback design based on precoding mse for mu miso systems for the separation of the signals in the vector broadcast channel bc some information about the channel state is necessary at the transmitter in many cases this channel state information csi must be fed back from the receivers to the transmitter we jointly design the channel estimators and the quantizers at the receivers together with the precoder at the transmitter based on a precoder centric criterion i e the minimization of a mean square error mse metric appropriate for the precoder design this is in contrast to our previous works where the quantizer design was based on a csi mse metric i e based on the minimization of the mse between the true channel and the channel recovered by the transmitter using a feedback channel interestingly the estimators resulting from this joint formulation are independent of the used codebook the codebook entries are the employed precoders therefore each receiver feeds back the index of a set of precoders and the intersection of the sets gives the appropriate precoder since the quantizers of the different receivers have to work separately the metric for the computation of the partition cells cannot be expressed as a simple squared error depending on the quantizer output the proposed system based on a joint optimization clearly outperforms previous designs with separate optimization of feedback and precoding

flow in rock fractures the local cubic law assumption reexamined we investigate the validity of applying the local cubic law lcl to flow in a fracture bounded by impermeable rock surfaces a two dimensional order of magnitude analysis of the navier stokes equations yields three conditions for the applicability of lcl flow as a leading order approximation in a local fracture segment with parallel or nonparallel walls these conditions demonstrate that the cubic law aperture should not be measured on a point by point basis but rather as an average over a certain length extending to the third dimension in addition to defining apertures over segment lengths we find that the geometry of the contact regions influences flow paths more significantly than might be expected from consideration of only the nominal area fraction of these contacts moreover this latter effect is enhanced by the presence of non lcl regions around these contacts while contact ratios of 0 1 0 2 are usually assumed to have a negligible effect our calculations suggest that contact ratios as low as 0 03 0 05 can be significant analysis of computer generated fractures with self affine walls demonstrates a nonlinear increase in contact area and a faster than cubic decrease in the overall hydraulic conductivity with decreasing fracture aperture these results are in accordance with existing experimental data on flow in fractures finally our analysis of fractures with self affine walls indicates that the aperture distribution is not lognormal or gamma as is frequently assumed but rather truncated normal initially and increasingly skewed with fracture closure

blind identification with periodic modulation a time domain approach we propose a method for blind identification of finite impulse response fir channels with periodic modulation the time domain formulation in terms of block signals is simple compared with existing frequency domain formulations it is shown that the linear equations relating the products of channel coefficients and the autocorrelation matrix of the received signal can be further arranged into decoupled groups the arrangement reduces computations and improves accuracy of the solution it also leads to very simple identifiability conditions and a very natural formulation of the optimal modulating sequence selection problem the proposed optimal selection minimizes the effects of channel noise and error in autocorrelation matrix estimation it results in a consistent channel estimate when the channel noise is white simulation results show that the method yields good performance it compares favorably with an existing subspace modulation induced cyclostationarity method and it is robust with respect to channel order overestimation the effect of modulation period and threshold of the modulating sequence are also discussed

auditory motivated level crossing approach to instantaneous frequency estimation we address the problem of estimating the instantaneous frequency if of a phase signal using its level crossing lc information based on front end auditory processing motivation we show that the problem of if estimation using lc information can be cast in the framework of estimation from irregularly sampled data the formulation has the generality of estimating different types of if without the need for a quasistationary assumption we consider two types of if polynomial and bandlimited we use polynomial interpolating functions for the former and for the latter we propose a novel line plus sum of sines model the model parameters are estimated by linear regression considering the noisy case lc data for different levels is analyzed and methods for combining different estimators from lcs are discussed theoretical and extensive simulation results show that the performance of the zero crossing zc based if estimator and the level crossing based if estimator with smaller level values is better than those obtained with higher level values or their combinations the new technique reaches the crame spl acute r rao bound crb roughly above 4 db signal to noise ratio snr and its performance does not deteriorate rapidly with mismatch in the if order compared with the other techniques in the literature

a least squares approach to blind channel identification conventional blind channel identification algorithms are based on channel outputs and knowledge of the probabilistic model of channel input in some practical applications however the input statistical model may not be known or there may not be sufficient data to obtain accurate enough estimates of certain statistics in this paper we consider the system input to be an unknown deterministic signal and study the problem of blind identification of multichannel fir systems without requiring the knowledge of the input statistical model a new blind identification algorithm based solely on the system outputs is proposed necessary and sufficient identifiability conditions in terms of the multichannel systems and the deterministic input signal are also presented

subspace partitioning for target detection and identification detection of a given target or set of targets from observed data is a problem countered in many applications regardless of the algorithm selected detection performance can be severely degraded when the subspace defined by the target data set is singular or ill conditioned high correlations between target components and their linear combinations lead to false positives and misidentifications especially for subspace based detectors in this paper we propose a subspace partitioning scheme that allows for detection to be performed in a number of better conditioned subspaces instead of the original subspace the proposed technique is applied to raman spectroscopic data analysis through both simulation and experimental results we demonstrate the improvement in the overall detection performance when using the proposed subspace partitioning scheme in conjunction with several subspace detection methods that are commonly used in practice

low complexity lmmse based mimo ofdm channel estimation via angle domain processing in many situations multiple input multiple output with orthogonal frequency division multiplexing mimo ofdm channels tend to be spatially correlated due for example to limited scattering prior knowledge of this channel spatial correlation and the channel frequency correlation can be exploited by using the linear minimum mean square error lmmse technique however the complexity of the 2 d lmmse technique which fully utilizes both the channel spatial and frequency correlation is quite high to solve this problem this paper presents and analyzes several low complexity suboptimal approximated lmmse channel estimation techniques in the angle domain where the channel model lends itself to a physical interpretation the choice of angle domain techniques is largely dependent on the extent of channel stochastic information e g channel correlation or power that is available to the receiver nevertheless all the proposed angle domain techniques have much lower complexity compared to the 2 d lmmse technique further all the angle domain techniques improve over the conventional least square ls technique for all the typical mimo ofdm models under consideration more importantly our simulation results show that the angle domain quasi 1 d ql d lmmse technique can achieve similar performance compared to the 2 d lmmse technique for all typical mimo ofdm models with significantly lower complexity

nonparametric estimation of specific occurrence exposure rate in risk and survival analysis abstract a cohort of individuals exposed to some risk is followed up to a point of time m and observations on two random variables y are recorded for each individual the variable refers to one of the four possible events that can occur for an individual in the period 0 m i dies of a specific disease say cancer ii dies of a natural cause iii withdraws from the study and iv is alive and still under study at time m the variable y refers to the time at which an event occurs based on such data for n individuals we consider the problem of estimation of a specific occurrence exposure rate soer which is a risk ratio defined as the ratio of probability of death due to cancer in the interval 0 m to the mean lifetime of all individuals up to the time point m the asymptotic distribution of a nonparametric estimator of soer is shown to be normal and the asymptotic variance involves unknown parameters various ways of bootstrapping are discussed for construction of confidence interva

periodic modulation based blind channel identification for single carrier block transmission with frequency domain equalization this paper proposes a periodic modulation based blind channel identification scheme for single carrier sc block transmission with frequency domain equalization fde the proposed approach relies on the block system model and exploits the circulant channel matrix structure after the cyclic prefix is removed it is shown that the set of linear equations relating the autocorrelation matrix of the block received signal and the product channel coefficients can be rearranged into one with a distinctive block circulant structure the identification equations thus obtained lead to a very simple identifiability condition as well as a natural formulation of the optimal modulating sequence design problem which based on the block circulant signal structure can be cast as a constrained quadratic problem that allows for a simple closed form solution the impact of the optimal modulating sequence on the peak to average power ratio papr is investigated also it is shown that the optimal sequence results in a consistent channel estimate irrespective of white noise perturbation pairwise error probability analysis is used to examine the equalization performance and based on which several design tradeoffs are discussed simulation results are used for illustrating the performance of the proposed method

fundamentals of statistical signal processing estimation theory minimum variance unbiased estimation cramer rao lower bound linear models general minimum variance unbiased estimation best linear unbiased estimators maximum likelihood estimation least squares method of moments the bayesian philosophy general bayesian estimators linear bayesian estimators kalman filters summary of estimators extension for complex data and parameters

transceiver optimization for block based multiple access through isi channels in this paper we describe a formulation of the minimum mean square error mmse joint transmitter receiver design problem for block based multiple access communication over intersymbol interference isi channels since the direct formulation of this problem turns out to be nonconvex we develop various alternative convex formulations using techniques of linear matrix inequalities lmis and second order cone programming socp in particular we show that the optimal mmse transceiver design problem can be reformulated as a semidefinite program sdp which can be solved using highly efficient interior point methods when the channel matrices are diagonal as in cyclic prefixed multicarrier systems we show that the optimal mmse transceivers can be obtained by subcarrier allocation and optimal power loading to each subcarrier for all the users moreover the optimal subcarrier allocation and power loading can be computed fairly simply in polynomial time by the relative ratios of the magnitudes of the subchannel gains corresponding to all subcarriers we also prove that any two users can share no more than one subcarrier in the optimal mmse transceivers by exploiting this property we design an efficient strongly polynomial time algorithm for the determination of optimal powerloading and subcarrier allocation in the two user case

the product limit estimator and the bootstrap some asymptotic representations the product limit estimator and its quantile process are represented as i i d mean processes with a remainder of ordern 3 4 logn 3 4 a s corresponding bootstrap versions of these representations are given which can help one visualize how the bootstrap procedure operates in this set up

fundamental limits of linear equalizers diversity capacity and complexity linear equalizers les have been widely adopted for practical systems due to their low computational complexity however it is also well known that les provide inferior performance relative to a maximum likelihood equalizer mle or other near mles because les usually cannot collect the diversity order enabled by the transmitter and at the same time they lose mutual information more importantly unlike mle or near mles the performance of les has not been well quantified this hinders more general applications of les in wireless systems in this paper we reveal a fundamental link between a channel parameter orthogonality deficiency od of the channel matrix and the diversity and capacity of les we identify that when the od of channel matrix has an upper bound strictly less than 1 the same diversity order as that of mles is collected by les and the outage capacity loss relative to mles is also a constant over signal to noise ratio snr these results can be applied to designing a framework for hybrid equalizers furthermore by studying the statistical properties of the od and comparing the complexity of different equalizers we show that hybrid equalizers can trade off between performance and complexity by tuning the channel matrix od the theoretical analysis is corroborated by computer simulations

fluid flow in synthetic rough walled fractures navier stokes stokes and local cubic law simulations 1 the results of three dimensional navier stokes ns and stokes simulations and two dimensional local cubic law lcl simulations of fluid flow through single rough walled fractures are presented synthetic rough walled fractures were created by combining random fields of aperture and the mean wall topography or midsurface which quantifies undulation about the fracture plane a finite volume formulation of the lcl that incorporates geometric corrections for fracture undulation is presented simulations of fluid flow through planar fractures with sinusoidal variation in aperture were compared to published results the rough walled fracture simulations demonstrated that the total flow rates predicted by the corrected lcl were within 10 of those predicted by the stokes equations for all the fractures examined in this work differences between the ns and stokes simulations clearly demonstrated that inertial forces can significantly influence the internal flow field within a fracture and the total flow rate across a fracture by limiting the total flow rate differences between the ns and stokes simulations constraints for three kinematic parameters were determined for all the fractures presented in this work the corrected lcl was determined to be an acceptable approximation to the ns equations provided that the kinematic and geometric constraints were met

on posterior distributions for signals in gaussian noise with unknown covariance matrix a bayesian approach to estimate parameters of signals embedded in complex gaussian noise with unknown color is presented the study specifically focuses on a bayesian treatment of the unknown noise covariance matrix making up a nuisance parameter in such problems by integrating out uncertainties regarding the noise color an enhanced ability to estimate both the signal parameters as well as properties of the error is exploited several noninformative priors for the covariance matrix such as the reference prior the jeffreys prior and modifications to this are considered some of the priors result in analytical solutions whereas others demand numerical approximations in the linear signal model connections are made between the standard adaptive maximum likelihood aml estimate and a bayesian solution using the jeffreys prior with adjustments to the jeffreys prior correspondence to the regularized solution is also established this in turn enables a formal treatment of the regularization parameter simulations indicate that significant improvements compared to the aml estimator can be obtained by considering both the derived regularized solutions as well as the one obtained using the reference prior the simulations also indicate the possibility of enhancing the predictions of properties of the error as uncertainties in the noise color are acknowledged

choosing the resampling scheme when bootstrapping a case study in reliability abstract often when dealing with complex data structures there is no unique way to bootstrap if the data can be viewed as u 1 u n iid from some distribution p then one can bootstrap by resampling the u s alternately one can resample in a more model based way that is by making use of the structure of the model p a typical example of this is linear regression in which the data is y i x i i 1 n one can resample the pairs y i x i or one can resample the residuals from a fitted model this phenomenon arises over quite a wide spectrum of problems and in many cases the different methods of bootstrapping can give substantially different results it seems hopeless to come up with a general theory that compares the different ways of bootstrapping in this article we study in some detail a certain model that arises in reliability theory in which there are two natural ways to bootstrap this model is described as follows available for testing is a sample of n iid systems each having t

estimation of flows in flow networks abstract let g be a directed graph with an unknown flow on each edge such that the following flow conservation constraint is maintained except for sources and sinks the sum of flows into a node equals the sum of flows going out of a node given a noisy measurement of the flow on each edge the problem we address which we call the m ost p robable f low e stimation problem mpfe is to estimate the most probable assignment of flow for every edge such that the flow conservation constraint is maintained we provide an algorithm called y mpfe for solving the mpfe problem when the measurement error is gaussian g aussian mpfe the algorithm works in o e v 2 when the underlying undirected graph of g is a 2 connected planar graph and in o e v when it is a 2 connected serial parallel graph or a tree this result is applicable to any m inimum c ost f low problem for which the cost function is e x e e 2 for edge e where e and e are constants and x e is the flow on edge e we show that for all topologies the g aussian mpfe s precision for each edge is analogous to the equivalent resistance measured in series to this edge in an electrical network built by replacing every edge with a resistor reflecting the measurement s precision on that edge

stochastic calculus as a tool in survival analysis a review this paper gives a brief survey of the uses of stochastic calculus in survival analysis the role played by martingale central limit theory in deriving asymptotic distributions of estimators and test statistics is described the nelson aalen estimator the kaplan meier estimator cox s proportional hazards model aalen s additive risk model and a goodness of fit test for cox s model are discussed sketches of the proofs of the main results are included

hybrid fm polynomial phase signal modeling parameter estimation and cramer rao bounds parameter estimation for a class of nonstationary signal models is addressed the class contains combination of a polynomial phase signal pps and a frequency modulated fm component of the sinusoidal or hyperbolic type such signals appear in radar and sonar applications involving moving targets with vibrating or rotating components a novel approach is proposed that allows us to decouple estimation of the fm parameters from those of the pps relying on properties of the multilag high order ambiguity function ml haf the accuracy achievable by any unbiased estimator of the hybrid fm pps parameters is investigated by means of the cramer rao lower bounds crlbs both exact and large sample approximate expressions of the bounds are derived and compared with the performance of the proposed methods based on monte carlo simulations

quasi ml period estimation from incomplete timing data given a noisy sequence of possibly shifted integer multiples of a certain period it is often of interest to accurately estimate the period with known integer regressors the problem is classical linear regression in many applications however the regressors are unknown integers and only loose bounds on the period are available examples include hop period and timing estimation wherein hops may be missed at the output of the frequency discriminator or the emitter may hop out of band pulse repetition interval pri analysis and passive rotating beam radio scanning we study several pertinent period estimators our emphasis is on a quasi maximum likelihood approach developed herein and an earlier method based on the fourier transform of a dirac delta train representation of the data surprisingly both are capable of attaining the clairvoyant crame spl acute r rao bound at moderate signal to noise ratios snrs even for short e g 10 samples we carefully address parameter identifiability issues and corroborate our findings with extensive simulations

new results on deterministic cramer rao bounds for real and complex parameters the cramer rao bounds crb is a lower bound of great interest for system analysis and design in the asymptotic region high signal to noise ratio snr and or large number of snapshots as it is simple to calculate and it is usually possible to obtain closed form expressions the first part of the paper is a generalization to complex parameters of the barankin rationale for deriving mse lower bounds that is the minimization of a norm under a set of linear constraints with the norm minimization approach the study of fisher information matrix fim singularity constrained crb and regularity conditions become straightforward corollaries of the derivation the second part provides new results useful for system analysis and design a general reparameterization inequality the equivalence between reparameterization and equality constraints and an explicit relationship between parameters unidentifiability and fim singularity

saturated flow in a single fracture evaluation of the reynolds equation in measured aperture fields fracture transmissivity and detailed aperture fields are measured in analog fractures specially designed to evaluate the utility of the reynolds equation the authors employ a light transmission technique with well defined accuracy approximately 1 error to measure aperture fields at high spatial resolution approximately 0 015 cm a hele shaw cell is used to confirm the approach by demonstrating agreement between experimental transmissivity simulated transmissivity on the measured aperture field and the parallel plate law in the two rough walled analog fractures considered the discrepancy between the experimental and numerical estimates of fracture transmissivity was sufficiently large approximately 22 47 to exclude numerical and experimental errors 2 as a source they conclude that the three dimensional character of the flow field is important for fully describing fluid flow in the two rough walled fractures considered and that the approach of depth averaging inherent in the formulation of the reynolds equation is inadequate they also explore the effects of spatial resolution aperture measurement technique and alternative definitions for link transmissivities in the finite difference formulation including some that contain corrections for tortuosity perpendicular to the mean fracture plane and stokes flow various formulations for link transmissivity are shown to converge at high resolution approximately 1 5 the spatial correlation length in the smoothly varying fracture at coarser resolutions the solution becomes increasingly sensitive to definition of link transmissivity and measurement technique aperture measurements that integrate over individual grid blocks were less sensitive to measurement scale and definition of link transmissivity than point sampling techniques