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We consider the problem of determining the Euclidean embedding of a dense, planar sensor network. The sensors are equipped with a binary sensing protocol that enables them to detect the neighboring sensors within a fixed radius, R. Using only this connectivity graph, we reconstruct an approximate embedding of the network on an Euclidean plane. To that end, we design an algorithm to identify special landmark nodes in the network whose Euclidean embedding is ``close'' to the vertices of an ideal hexagonal lattice.


This work investigates the parameter estimation performance of super-resolution line spectral estimation using atomic norm minimization. The focus is on analyzing the algorithm's accuracy of inferring the frequencies and complex magnitudes from noisy observations. When the Signal-to-Noise Ratio is reasonably high and the true frequencies are separated by $O(\frac{1}{n})$, the atomic norm estimator is shown to localize the correct number of frequencies, each within a neighborhood of size $O(\sqrt{\frac{\log n}{n^3}} \sigma)$ of one of the true frequencies.


In array processing, mutual coupling between sensors has an adverse effect on the estimation of parameters (e.g., DOA). Sparse arrays, such as nested arrays, coprime arrays, and minimum redundancy arrays (MRAs), have reduced mutual coupling compared to uniform linear arrays (ULAs). With $N$ denoting the number of sensors, these sparse arrays offer $O(N^2)$ freedoms for source estimation because their difference coarrays have $O(N^2)$-long ULA segments.


we present a new Time-Frequency approach
for recovering sources’ contribution to two convolutive
mixtures. The separation task is performed on two steps:
Each mixture is clustered into Voiced/Unvoiced frames, and
then the predominant source in each time frequency bin is
identified through a specific weight function which is based
on sources’ excitation characteristics extraction. We investigate
the performance of the proposed approach in the underdetermined
context using objective quality measures.


How accurately can one estimate a deterministic parameter subject to other unknown deterministic model parameters? The most popular answer to this question is given by the Cramer-Rao bound (CRB). The main assumption behind the derivation of the CRB is local unbiased estimation of all model parameters. The foundations of this work rely on doubting this assumption. Each parameter in its turn is treated as a single parameter of interest, while the other model parameters are treated as nuisance, as their mis-knowledge interferes with the estimation of the parameter of interest.


This work presents a new array geometry, which is capable of providing $O(M^2N^2)$ degrees of freedom (DOF) using only $MN$ physical sensors via utilizing the second-order statistics of the received data. This new array is composed of multiple, identical minimum redundancy subarrays, whose positions follow a minimum redundancy configuration. Thus the new array is a minimum redundancy array (MRA) of MRA subarrays, and is termed {\em nested MRA}. The sensor positions, aperture length, and the number of DOF of the new array can be predicted if these parameters of MRA subarrays are given.


Spherical harmonics root-MUSIC (MUltiple SIgnal Classification) technique for source localization using spherical microphone array is presented in this paper. Earlier work on root-MUSIC is limited to linear and planar arrays. Root-MUSIC for planar array utilizes the concept of manifold separation and beamspace transformation. In this paper, the Vandermonde structure of array manifold for a particular order is proved. Hence, the validity of root-MUSIC in the spherical harmonics domain is confirmed. The proposed method is evaluated by using simulated experiments on source localization.


We address the problem of terrain-scattered jammer suppression in multiple-input multiple-output (MIMO) radar using space-(fast) time adaptive processing (SFTAP). The correlation function of jamming components after matched filtering at the receiving end of MIMO radar is derived, and its relationship to the correlation matrix of the transmitted waveforms is established. This correlation function serves as a theoretical measure of evaluating the matched filtering effect on the received jamming signals.