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In this paper, a novel modified complex multi-task Bayesian compressive sensing (MCMT-BCS) algorithm is proposed to acquire high-resolution images in stepped-frequency through-the-wall radar imaging (TWRI) exploiting multipath. Unlike traditional TWRI approaches that assume frequency-independent scattering model, we develop a practical subband scattering(More)
—Through-the-wall imaging and urban sensing is an emerging area of research and development. The incorporation of the effects of signal propagation through wall material in producing an indoor image is important for reliable through-the-wall mission operations. We have previously analyzed wall effects, such as refraction and change in propagation speed, and(More)
In this paper, we examine the time-frequency representation (TFR) and sparse reconstruction of non-stationary signals in the presence of missing data samples. These samples lend themselves to missing entries in the instantaneous auto-correlation function (IAF) which, in turn, induce artifacts in the time-frequency distribution and ambiguity function. The(More)
—This paper studies the optimum performance boundaries of a two-hop multi-antenna amplify-and-forward (AF) relay system with a multi-antenna energy harvesting (EH) receiver. The source and relay nodes employ orthogonal space-time block codes for data transmission. When instantaneous channel state information (CSI) is available, we design joint optimal(More)
Spatial time-frequency distributions (STFDs) have been recently introduced as the natural means to deal with source signals that are localiz-able in the time-frequency domain. It has been shown that improved estimates of the signal and noise subspaces are achieved by constructing the subspaces from the time-frequency signatures of the signal arrivals rather(More)
We address the problem of blind source separation of non-stationary signals of which only instantaneous linear mixtures are observed. A blind source separation approach exploiting both auto-terms and cross-terms of the time-frequency (TF) distributions of the sources is considered. The approach is based on the simultaneous diago-nalization and(More)
In this paper, we propose a Bayesian compressive sensing algorithm for effective reconstruction of sparse signals that demonstrate sparsity as continuous but irregular narrow strips in a multi-dimensional space. Among many applications of this class of representations are the two-dimensional time-frequency distributions (TFDs) of radar signals, which are(More)