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In this paper, we are interested in the high-resolution
imaging of an unknown area based on only power measurements
of a small number of wireless transceivers located on one
side of the unknown area. In order to do so, we propose a
framework that achieves a polynomial order reduction in the
number of antennas required for high-resolution imaging. More
specifically, we show that by spacing the antennas at multiples
of the wavelength and applying subspace-based analysis, we can
image M targets using only 2M+1 transmit/receive antennas (as


Sparse sensor arrays can achieve significantly more degrees of freedom than the number of elements by leveraging the co-array, a virtual structure that arises from the far field narrowband signal model. Although several sparse array configurations have been developed for passive sensing tasks, less attention has been paid to arrays suitable for active sensing. This paper presents a novel active sparse linear array, called the Interleaved Wichmann Array (IWA). The IWA only has a few closely spaced elements, which may make it more robust to mutual coupling effects.


Societal acceptance of self-driving cars (SDC) is predicated on a level of trust between humans and the au- tonomous vehicle. Although the performance of SDCs has im- proved dramatically, the question of mainstream acceptance and requisite trust is still open. We are exploring this question through integration of virtual reality SDC simulator and an electroencephalographic (EEG) recorder. In order for a passenger to build and maintain trust, the SDC will need to operate in a manner that elicits positive emotional response and avoids negative emotional response.


Distributed calibration based on consensus optimization is a computationally efficient method to calibrate large radio interferometers such as LOFAR and SKA. Calibrating along multiple directions in the sky and removing the bright foreground signal is a crucial step in many science cases in radio interferometry. The residual data contain weak signals of huge scientific interest and of particular concern is the effect of incomplete sky models used in calibration on the residual. In order to study this, we consider the mapping between the input uncalibrated data and the output residual data.


In the partial relaxation approach, at each desired direction, the manifold structure of the remaining interfering signals impinging on the sensor array is relaxed, which results in closed form estimates for the interference parameters. By adopting this approach, in this paper, a new estimator based on the unconstrained covariance fitting problem is proposed. To obtain the null-spectra efficiently, an iterative rooting scheme based on the rational function approximation is applied.


The most state-of-art time-difference-of-arrival (TDOA) localization algorithms are performed under the assumption that all the nodes are synchronized. However, for a widely distributed wireless sensor networks (WSNs), time synchronization between all the nodes is not a trival problem. In this paper, we study the problem of source localization using signal TDOA measurements in the system of nodes part synchronization. Starting from the maximum likelihood estimator (MLE), we develop a semidefinite programming (SDP) approach.


Acoustical behavior of a room for a given position of microphone and sound source is usually described using the room impulse response. If we rely on the standard uniform sampling, the estimation of room impulse response for arbitrary positions in the room requires a large number of measurements. In order to lower the required sampling rate, some solutions have emerged that exploit the sparse representation of the room wavefield in the terms of plane waves in the low-frequency domain. The plane wave representation has a simple form in rectangular rooms.


A method is proposed for estimating the source signal and its direction of arrival (DOA) in this paper. It is based on ML estimation of the transfer function between microphones combined with the EM algorithm for a Gaussian Mixture Model (GMM), assuming that the signal is captured at each microphone with delay corresponding to the traveling of sound and some decay. By this modeling, search for the maximum log-likelihood in the ML estimation can be realized simply by eigenvalue decomposition of a properly designed matrix.


This paper formulates the general Adapt-then-Combine (ATC) and Random Exchange (RndEx) diffusion filters for an arbitrary nonlinear state-space model. Subsequently, we propose two novel marginal Particle Filter implementations of the general ATC and RndEx filters using respectively a pure Sequential Monte Carlo (SMC) strategy and a hybrid Gaussian/SMC methodology. The proposed algorithms are assessed via simulation in a numerical example of cooperative target tracking with received-signal-strength (RSS) sensors.