Abstract Assume that observations are generated from a nonstationary autoregressive (AR) processes of infinite order. We adopt a finite-order approximation model to predict future observations and obtain an asymptotic expression for the mean-squared prediction error (MSPE) of the least squares predictor. This expression provides the first exact assessment of the impacts of nonstationarity, mode...

In this paper we consider the problem of distributed adaptive estimation in wireless sensor networks for two different observation noise conditions. In the first case, we assume that there are some sensors with high observation noise variance (noisy sensors) in the network. In the second case, different variance for observation noise is assumed among the sensors which is more close to real scen...

This paper studies the eeect of array calibration errors on the performance of various DF (direction nding) based signal copy algorithms. Unlike blind copy methods, this class of algorithms requires an estimate of the directions of arrival (DOAs) of the signals in order to compute the copy weight vectors. Under the assumption that the observation time is suuciently long, the following algorithm...

boundary integral equations (bie) are reformulations of boundary value problems for partial differential equations. there is a plethora of research on numerical methods for all types of these equations such as solving by discretization which includes numerical integration. in this paper, the neumann problem is reformulated to a bie, and then moving least squares as a meshless method is describe...

An outlier is an observation that deviates markedly from the majority of the data. To know which observation has greater influence on parameter estimate, detection of outlier is very important. There are several methods for detection of outliers available in the literature. A good number of test-statistics for detecting outliers have been developed. In contrast to detection, outliers are also t...

H 1 optimal estimators guarantee the smallest possible estimation error energy over all possible disturbances of xed energy, and are therefore robust with respect to model uncertainties and lack of statistical information on the exoge-nous signals. We have recently shown that if prediction error is considered, then the celebrated LMS adaptive l-tering algorithm is H 1 optimal. In this paper we ...

Synthetic Aperture Radar Interferometry (InSAR) techniques are increasingly applied for monitoring land subsidence. The advantages of InSAR include high accuracy and the ability to cover large areas; nevertheless, research validating the use of InSAR on building deformation is limited. In this paper, we test the monitoring capability of the InSAR in experiments using two landmark buildings; the...

This paper studies the effect of array manifold errors on ihe performance of various signal copy algorithms. under t h e assumption that the observation time is suficiently long, the following algorithms are atudied: classical beamforming, least squares, total least squares, linearly constrained minimum variance beamforming, and structured stochastic estimation. Expressions JOT the mean-square ...

A nonlinear regression model with correlated, normally distributed errors with non zero means is investigated. The limit properties of bias and the mean square error matrix of the approximate least squares estimator of regression parameters are studied.