IEEE Transactions on Signal Processing

A major drawback of subspace methods for direction-of-arrival estimation is their poor performance in the presence of coherent sources. Spatial smoothing is a common solution that can be used to restore the performance of these methods in such a case at the cost of increased array size requirement. In this paper, a Hadamard product perspective of the source resolvability problem of spatial-smoothing-based subspace methods is presented.

We consider the problem of stochastic optimization with nonlinear constraints, where the decision variable is not vector-valued but instead a function belonging to a reproducing Kernel Hilbert Space (RKHS). Currently, there exist solutions to only special cases of this problem.

The theoretical basis for conventional acquisition of bandlimited signals typically relies on uniform time sampling and assumes infinite-precision amplitude values. In this paper, we explore signal representation and recovery based on uniform amplitude sampling with assumed infinite precision timing information. 

The IEEE Signal Processing Society congratulates the following recipients who will receive the 2018 IEEE Signal Processing Society paper awards for their paper published in the IEEE Transactions on Signal Processing. Presentation of the paper awards will take place at ICASSP 2019 in Brighton, U.K.

A task of major practical importance in network science is inferring the graph structure from noisy observations at a subset of nodes. Available methods for topology inference typically assume that the process over the network is observed at all nodes. However, application-specific constraints may prevent acquiring network-wide observations.

This paper discusses greedy methods for sensor placement in linear inverse problems. We comprehensively review the greedy methods in the sense of optimizing the mean squared error (MSE), the volume of the confidence ellipsoid, and the worst-case error variance. We show that the greedy method of optimizing an MSE related cost function can find a near-optimal solution.

Linear canonical transforms (LCTs) are of importance in many areas of science and engineering with many applications. Therefore, a satisfactory discrete implementation is of considerable interest. Although there are methods that link the samples of the input signal to the samples of the linear canonical transformed output signal, no widely-accepted definition of the discrete LCT has been established.

Radio tomographic imaging (RTI) is an emerging technology to locate physical objects in a geographical area covered by wireless networks. From the attenuation measurements collected at spatially distributed sensors, radio tomography capitalizes on spatial loss fields (SLFs) measuring the absorption of radio frequency waves at each location along the propagation path.

In this paper, we propose a regular vine copula based methodology for the fusion of correlated decisions. Regular vine copula is an extremely flexible and powerful graphical model to characterize complex dependence among multiple modalities.

This paper addresses the design and analysis of feedback-based online algorithms to control systems or networked systems based on performance objectives and engineering constraints that may evolve over time. The emerging time-varying convex optimization formalism is leveraged to model optimal operational trajectories of the systems, as well as explicit local and network-level operational constraints.