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With the increase of pulse density, signal sorting becomes extremely difficult for modern electronic reconnaissance, especially for pulse-compression radar signals. Blind source separation (BSS) is a new developed technology for separating signals from mixed observed data. In this paper, we propose various instantaneous mixing models of pulse-compression radar signals, including linear frequency modulation,...
The convolutive blind source separation (BSS) problem can be solved in frequency domain. To solve the permutation ambiguity problem in frequency domain, this paper presents an improved permutation alignment algorithm. According to features of radar signals, first the frequency domain is divided to some region segmentations. Then the permutation alignment is performed in each region independently....
Since in many blind source separation applications, latent sources are both non-Gaussian and have sample dependence, it is desirable to exploit both non-Gaussianity and sample dependency. In this paper, we use the Markov model to construct a general framework for the analysis and derivation of algorithms that take both properties into account. We also present two algorithms using two effective source...
This paper considers the problem of joint blind source separation (J-BSS), which appears in many practical problems such as blind deconvolution or functional magnetic resonance imaging (fMRI). In particular, we establish the necessary and sufficient conditions for the solution of the J-BSS problem by exclusively exploiting the second-order statistics (SOS) of the observations. The identifiability...
Independent component analysis (ICA) has proven useful for the analysis of functional magnetic resonance imaging (fMRI) data. In this paper, we compare the performance of three ICA algorithms and show the importance of taking sample correlation information into account. The three ICA algorithms are Infomax, the most widely used algorithm for fMRI analysis, entropy bound minimization (EBM) that adapts...
We propose a new entropy rate estimator for a second and/or higher-order correlated source by modeling it as the output of a linear filter, which can be mixed-phase, driven by Gaussian or non-Gaussian noise. Based on this estimator, we develop a new spatiotemporal blind source separation (BSS) algorithm, full BSS (FBSS), by minimizing the entropy rate of separated sources. FBSS provides more flexibilities...
We present a new (differential) entropy estimator where the maximum entropy bound is used to approximate the entropy given the observations, and is computed using a numerical procedure. The resulting accurate estimate for the entropy is used to derive a new algorithm to perform independent component analysis (ICA). The new algorithm, ICA by entropy bound minimization (ICA-EBM), adopts a line search...
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