Dionysios S. Kalogerias

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It was recently shown that low rank matrix completion theory can be employed for designing new sampling schemes in the context of MIMO radars, which can lead to the reduction of the high volume of data typically required for accurate target detection and estimation. Employing random samplers at each reception antenna, a partially observed version of the(More)
In this paper, we investigate a novel networked colocated MIMO radar approach that relies on sparse sensing and matrix completion, and enables significant reduction of the volume of data required for accurate target detection and estimation. More specifically, the receive antennas sample the target returns via two sparse sensing schemes, and forward the(More)
We consider a source (Alice) trying to communicate with a destination (Bob), in a way that an unauthorized node (Eve) cannot infer, based on her observations, the information that is being transmitted. The communication is assisted by multiple multi-antenna cooperating nodes (helpers) who have the ability to move. While Alice transmits, the helpers transmit(More)
It was recently shown that low rank Matrix Completion (MC) theory can support the design of new sampling schemes in the context of MIMO radars, enabling significant reduction of the volume of data required for accurate target detection and estimation. Based on the data received, a matrix can be formulated, which can then be used in standard array processing(More)
We consider the problem of approximating optimal in the Minimum Mean Squared Error (MMSE) sense nonlinear filters in a discrete time setting, exploiting properties of stochastically convergent state process approximations. More specifically, we consider a class of nonlinear, partially observable stochastic systems, comprised by a (possibly nonstationary)(More)
We consider a cooperative secret communication scenario, in which a group of mobile and power constrained nodes, acting as relays, cooperatively transmit to a destination in the presence of an eavesdropper; both destination and eavesdropper are assumed stationary. The cooperative scheme entails motion control and optimal communication, in order to achieve a(More)
—We propose a nonlinear filtering framework for approaching the problems of channel state tracking and spa-tiotemporal channel gain prediction in mobile wireless sensor networks, in a Bayesian setting. We assume that the wireless channel constitutes an observable (by the sensors/network nodes), spatiotemporal, conditionally Gaussian stochastic process,(More)
This paper revisits grid based recursive approximate filtering of general Markov processes in discrete time, partially observed in conditionally Gaussian noise. The grid based filters considered rely on two types of state quantization, namely, the Markovian type and the marginal type. A set of novel, relaxed sufficient conditions is proposed, ensuring(More)
We propose a nonlinear filtering framework for channel state tracking and spatiotemporal channel gain prediction in mobile wireless sensor networks, in a Bayesian setting. Under common assumptions, the wireless channel constitutes an observable (by the sensors/network nodes), spatiotemporal, conditionally Gaussian stochastic process, which is statistically(More)