Amplitude estimation of a signal with known waveform in the presence of steering vector uncertainties

In this correspondence, we address the problem of estimating the amplitude of a signal with known waveform received on an array of sensors and we consider the case where there exist uncertainties about the spatial signature of the signal of interest. Closed-form expressions for the Cramer–Rao bound are derived and the respective influence of the uncertainties and the number of snapshots is studied. The maximum likelihood estimator (MLE) of the signal of interest amplitude along with the covariance matrix of the interferences and noise is also derived and an iterative algorithm is presented to obtain the ML estimates

Generalized multivariate analysis of variance - A unified framework for signal processing in correlated noise

Generalized multivariate analysis of variance (GMANOVA) and related reduced-rank regression are general statistical models that comprise versions of regression, canonical correlation, and profile analyses as well as analysis of variance (ANOVA) and covariance in univariate and multivariate settings. It is a powerful and, yet, not very well-known tool. We develop a unified framework for explaining, analyzing, and extending signal processing methods based on GMANOVA. We show the applicability of this framework to a number of detection and estimation problems in signal processing and communications and provide new and simple ways to derive numerous existing algorithms. Many of the methods were originally derived from scratch , without knowledge of their close relationship with the GMANOVA model. We explicitly show this relationship and present new insights and guidelines for generalizing these methods. Our results could inspire applications of the general framework of GMANOVA to new problems in signal processing. We present such an application to flaw detection in nondestructive evaluation (NDE) of materials. A promising area for future growth is image processing

Parameterized maximum likelihood method (PML): application to space-time radar localization

We present a maximum likelihood method for the localization of sources with known waveforms . It's a joint space time radar localization which is a generalisation of recent methods to coherent signal . The obtained results are usefull in wireless communications for the identification of propagation channel with a pilot signal . An exact maximum likelihood method is presented . Variances of estimation and related Cramer Rao Bound are established . Simulations results illustrate the behaviour of the algorithm.Nous présentons une technique du maximum de vraisemblance qui localise des sources dont les formes d'ondes sont identiques et connues. Il s'agit d'une localisation radar conjointe direction-retard qui est une extension aux cas de signaux cohérents des méthodes actuellement utilisées et exploitant la connaissance des signaux émis. Les résultats obtenus s'appliquent de la même manière aux cas des communications mobiles pour lesquelles on veut identifier le canal de propagation à l'aide d'un signal connu. Un estimateur exact du maximum de vraisemblance est présenté. Les variances d'estimation ainsi que les bornes de Cramer-Rao sont établies. Des résultats de simulations viennent illustrer le comportement des algorithmes pour lesquels les performances sont comparées à la borne de Cramer-Rao

Some results on the Weiss-Weinstein bound for conditional and unconditional signal models in array processing

International audienceIn this paper, the Weiss-Weinstein bound is analyzed in the context of sources localization with a planar array of sensors. Both conditional and unconditional source signal models are studied. First, some results are given in the multiple sources context without specifying the structure of the steering matrix and of the noise covariance matrix. Moreover, the case of an uniform or Gaussian prior are analyzed. Second, these results are applied to the particular case of a single source for two kinds of array geometries: a non-uniform linear array (elevation only) and an arbitrary planar (azimuth and elevation) array

Decoupled maximum likelihood channel estimator for space-time block coded system

Master'sMASTER OF ENGINEERIN

Multidimensional Frequency Estimation with Applications in Automotive Radar

This thesis considers multidimensional frequency estimation with a focus on computational efficiency and high-resolution capability. A novel framework on multidimensional high-resolution frequency estimation is developed and applied to increase the range, radial velocity, and angular resolution capcability of state-of-the-art automotive radars