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Limiting spectral distribution of the sample covariance matrix of the windowed array data
, Article Eurasip Journal on Advances in Signal Processing ; Volume 2013, Issue 1 , 2013 ; 16876172 (ISSN) ; Gazor, S ; Bastani, M. H ; Sharif University of Technology
2013
Abstract
In this article, we investigate the limiting spectral distribution of the sample covariance matrix (SCM) of weighted/windowed complex data. We use recent advances in random matrix theory and describe the distribution of eigenvalues of the doubly correlated Wishart matrices. We obtain an approximation for the spectral distribution of the SCM obtained from windowed data. We also determine a condition on the coefficients of the window, under which the fragmentation of the support of noise eigenvalues can be avoided, in the noise-only data case. For the commonly used exponential window, we derive an explicit expression for the l.s.d of the noise-only data. In addition, we present a method to...
Source Enumeration and Identification in Array Processing Systems
, Ph.D. Dissertation Sharif University of Technology ; Bastani, Mohammad Hasan (Supervisor)
Abstract
Employing array of antennas in amny signal processing application has received considerable attention in recent years due to major advances in design and implementation of large dimentional antennas. In many applications we deal with such large dimentional antennas which challenge the traditional signal processing algorithms. Since most of traditional signal processing algorithms assume that the number of samples is much more than the number of array elements while it is not possible to collect so many samples due to hardware and time constraints.
In this thesis we exploit new results in random matrix theory to charachterize and describe the properties of Sample Covariance Matrices...
In this thesis we exploit new results in random matrix theory to charachterize and describe the properties of Sample Covariance Matrices...
Source enumeration in large arrays based on moments of eigenvalues in sample starved conditions
, Article IEEE Workshop on Signal Processing Systems, SiPS: Design and Implementation, 17 October 2012 through 19 October 2012, Quebec ; October , 2012 , Pages 79-84 ; 15206130 (ISSN) ; 9780769548562 (ISBN) ; Bastani, M. H ; Gazor, S ; Sharif University of Technology
2012
Abstract
This paper presents a scheme to enumerate the incident waves impinging on a high dimensional uniform linear array using relatively few samples. The approach is based on Minimum Description Length (MDL) criteria and statistical properties of eigenvalues of the Sample Covariance Matrix (SCM). We assume that several models, with each model representing a certain number of sources, will compete and MDL criterion will select the best model with the minimum model complexity and maximum model decision. Statistics of noise eigenvalue of SCM can be approximated by the distributional properties of the eigenvalues given by Marcenko-Pastur distribution in the signal-free SCM. In this paper we use random...
Statistical performance analysis of MDL source enumeration in array processing
, Article IEEE Transactions on Signal Processing ; Volume 58, Issue 1 , 2010 , Pages 452-457 ; 1053587X (ISSN) ; Malek Mohammadi, M ; Nayebi, M. M ; Aref, M. R ; Sharif University of Technology
Abstract
In this correspondence, we focus on the performance analysis of the widely-used minimum description length (MDL) source enumeration technique in array processing. Unfortunately, available theoretical analysis exhibit deviation from the simulation results. We present an accurate and insightful performance analysis for the probability of missed detection.We also show that the statistical performance of the MDL is approximately the same under both deterministic and stochastic signal models. Simulation results show the superiority of the proposed analysis over available results