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Numerical aspects of least squares estimation have not been sufficiently studied in the literature. In particular, information matrix has a large condition number for systems with harmonic regressor in the initial steps of RLS (Recursive Least Squares) estimation. A large condition number indicates invertibility problems and necessitates the development of new algorithms with improved accuracy of...
This paper addresses the problem of identifying errors-in-variables models, where the both input and output measurements are corrupted by white noise. The Koopmans-Levin method, which is a computationally simple consistent estimation method for errors-in-variables situations, requires a priori knowledge about the values of variances or the ratio to measurement noises. To achieve the consistent estimation...
We study the design of minimum variance portfolio when asset returns follow a low rank factor model. Using results from random matrix theory, an optimal shrinkage approach for the isolated eigenvalues of the covariance matrix is developed. The proposed portfolio optimization strategy is shown to have good performance on synthetic data but not always on real data sets. This leads us to refine the data...
Anomaly detection aims to detect sources with different spectral characteristics from the background in an hyperspectral image. Classical tools for anomaly detection and estimation are known to have poor performance when they are used on high dimensional hyperspectral image since typically both the number of available sample and their size are large for this kind of imaging. New estimation methods...
RSSI gives an initial rough measure of the inter node distances at low cost without the need of additional equipment or complexity. This necessitates the need for a mechanism to obtain accurate node locations from the noisy distance estimates. Manifold learning techniques can be used for estimating locations, but their ability to localize node in the sensor network environment has not been benchmarked...
Unmixing of Hyperspectral images is essentially a blind or semi-blind source separation problem that tries to extract object (endmember) spectra and abundance from the image which is considered as a linear or non-linear mixture of spectrally distinct objects (endmembers) weighted by fractional abundance. An accurate and efficient method for estimation of number of endmembers present in the image scene...
Many papers in the hyperspectral literature use simulations (based on a linear mixture model) to test algorithms which either estimate the dimensionality of the data or endmem-bers. Typically these simulations use (i) “real world” end-members, (ii) proportions distributed according to a uniform or Dirichlet distribution on the endmember simplex, and (iii) Gaussian errors which are “spectrally” and...
The State Estimation (SE) problem in electric power systems consists of three main functions: estimation, bad data detection and identification. D'Antona formalized the estimation procedure considering the contribution of both the measurement and the network parameters to uncertainty, in the so called extended SE. This paper presents an investigation of the effectiveness of data detection and identification...
Direction of arrival (DOA) estimation has been a vital area of research since few decades due to its importance in applications like wireless communication, navigation radar, sonar etc. The resolution being one of the main challenges in the direction of arrival estimation can be enhanced by array antenna system with reliable signal processing. In this paper we simulate the high resolution subspace...
An accurate estimation of the transmit-antenna number is a prerequisite for blind signal detection in non-collaborated multiple-input multiple-output (MIMO) systems. This paper proposes a Wishart-matrix's largest eigenvalue (WME) based hypothesis testing algorithm for blind detecting the transmit-antenna number. By introducing the Random Matrix Theory (RMT), we can get a precise distribution of the...
Outlier labeling can be considered as an early procedure to get the information of ‘suspects’. This paper introducesrobust kurtosis projection algorithm for multivariate outlier labeling of data set with moderate, high and very high percentage outlier. The algorithm works in two stages. In the first stage, we propose a projection approach to findthe orthonormal set of all vectors that maximize the...
State estimation of a non-linear system perturbed by non Gaussian noise is a challenging task. Typical solutions like EKF/UKF could fail while Monte Carlo methods, even though more accurate, are computationally expensive. Recently proposed log homotopy based particle flow filter, also known as Daum-Huang filter (DHF) provides an alternative way of non-linear state estimation. There have been a number...
In this paper, we provide a new viewpoint of sequential random processes of the kind F(x), where x is a multivariate vector of covariates, in terms of a smoothing operation governed by a covariance function. By exploiting the eigenvalues and eigenvectors of the covariance function, we represent the smooth function in terms of an orthogonal series over basis functions where the basis function weights...
In order to improve the speed of the DOA estimation, an efficient MUSIC algorithm using subspace projection is proposed in this paper. In the algorithm, the covariance matrix, which causes high computational complexity in the subspace projection (SP) algorithm of subspace tracking field, is approximated to simplify the processing procedure. The proposed algorithm shows similar angle accuracy compared...
The underground transmission velocity of electromagnetic wave is the key parameter for synthetic aperture (SA) imaging algorithm of ground penetrating radar (GPR). The traditional estimation on underground electromagnetic wave transmission velocity adopts the method of measuring dielectric constant. However, this kind of method is often very inconvenient in practical application, especially when underground...
As an extension of the conventional Fourier transform and as a time-frequency signal analysis tool, the fractional Fourier transforms (FRFT) are suitable for dealing with various types of non-stationary signals. Computation of the discrete fractional Fourier transform (DFRFT) and its chirp concentration properties are both dependent on the basis of DFT eigenvectors used in the computation. Several...
We introduce an original algorithm to perform the joint eigen value decomposition of a set of real matrices. The proposed algorithm is iterative but does not resort to any sweeping procedure such as classical Jacobi approaches. Instead we use a first order approximation of the inverse of the matrix of eigen vectors and at each iteration the whole matrix of eigenvectors is updated. This algorithm is...
Array radio telescopes are suitable for the implementation of spatial filters. These filters present the advantage of canceling potential radio frequency interference (RFI) while recovering uncorrupted Time-Frequency data, of interest to astronomers. Although information regarding the sources of RFI can be a priori known or reliably inferred, the complexity of radio telescope systems randomizes the...
In this paper, a novel array-based method to estimate the path loss exponent (PLE) is developed. The method is designed as a part of an automatic calibration step, prior to localization of a source transmitting in the near-far field of the array. The method only requires the knowledge of the ranges between the array elements. By making the antenna elements transmit in turn, the array response model...
Multichannel noise reduction can be achieved without dis torting the desired signals, provided that the relative transfer functions (RTFs) of the sources are known. Many RTF esti mators require periods where only one source is active, which limits their applicability in practice. We propose an RTF esti mator that does not require such periods. A time-varying RTF is computed per time-frequency (TF)...
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