<![CDATA[ IEEE Transactions on Signal Processing - new TOC ]]>
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TOC Alert for Publication# 78 2016July 28<![CDATA[Super-Resolution Compressed Sensing for Line Spectral Estimation: An Iterative Reweighted Approach]]>6418464946621202<![CDATA[Multi-Parameter Estimation in Compound Gaussian Clutter by Variational Bayesian]]>641846634678839<![CDATA[Inhomogeneous Poisson Sampling of Finite-Energy Signals With Uncertainties in <inline-formula> <tex-math notation="LaTeX">${mathbb{R}}^{d}$</tex-math></inline-formula>]]>6418467946943337<![CDATA[Limited Rate Feedback in a MIMO Wiretap Channel With a Cooperative Jammer]]>641846954706721<![CDATA[Joint Design of Transmit Waveforms and Receive Filters for MIMO Radar Space-Time Adaptive Processing]]>6418470747221405<![CDATA[Nonparametric Bayesian Attributed Scattering Center Extraction for Synthetic Aperture Radar Targets]]>6418472347361427<![CDATA[Robust Hypothesis Testing with <inline-formula><tex-math notation="LaTeX">$alpha $</tex-math> </inline-formula>-Divergence]]>$alpha $ —divergence distances, is proposed. Sion's minimax theorem is adopted to characterize the saddle value condition. Least favorable distributions, the robust decision rule and the robust likelihood ratio test are derived. If the nominal probability distributions satisfy a symmetry condition, the design procedure is shown to be simplified considerably. The parameters controlling the degree of robustness are bounded from above and the bounds are shown to be resulting from a solution of a set of equations. The simulations performed evaluate and exemplify the theoretical derivations.]]>6418473747501574<![CDATA[Closed-Form and Near Closed-Form Solutions for TOA-Based Joint Source and Sensor Localization]]>6418475147661120<![CDATA[Efficient Algorithms on Robust Low-Rank Matrix Completion Against Outliers]]>641847674780778<![CDATA[Steady-State Statistical Performance Analysis of Subspace Tracking Methods]]>6418478147911212<![CDATA[A Batch Algorithm for Estimating Trajectories of Point Targets Using Expectation Maximization]]>$N$-scan pruning based track-oriented multiple hypothesis tracking (TOMHT). The proposed algorithm shows a good tradeoff between computational complexity and the MOSPA performance.]]>641847924804855<![CDATA[An Iterative Reweighted Method for Tucker Decomposition of Incomplete Tensors]]>$N$th-order tensor in terms of $N$ factor matrices and a core tensor via multilinear operations. To exploit the underlying multilinear low-rank structure in high-dimensional datasets, we propose a group-based log-sum penalty functional to place structural sparsity over the core tensor, which leads to a compact representation with smallest core tensor. The proposed method is developed by iteratively minimizing a surrogate function that majorizes the original objective function. This iterative optimization leads to an iteratively reweighted least squares algorithm. In addition, to reduce the computational complexity, an over-relaxed monotone fast iterative shrinkage-thresholding technique is adapted and embedded in the iterative reweighted process. The proposed method is able to determine the model complexity (i.e., multilinear rank) in an automatic way. Simulation results show that the proposed algorithm offers competitive performance compared with other existing algorithms.]]>641848174829771