6 accepted papers
We study the problem of noisy sparse array interpolation, where a large virtual array is synthetically generated by interpolating missing sensors using matrix completion techniques that promote low rank. The current understanding is quite limited regarding the effect of the (sparse) array geometry o…
We study single-snapshot nested array interpolation with positive sources. The problem of sparse array interpolation is traditionally cast as a low-rank Toeplitz/Hankel matrix completion problem from partial observations. In recent work, we provided the first necessary and sufficient guarantees for…
This paper considers the problem of recovering the joint support (of size K) of a set of unknown sparse vectors in ℝ <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">d</sup> , each of which can be sensed using a different measurement matrix. Such models…
This paper proposes a novel method to focus or align array manifolds at different frequencies to a single reference frequency in wideband direction of arrival (DOA) estimation. Unlike existing methods, our focusing can be performed without explicitly constructing focusing matrices, or requiring any…
This paper considers the problem of recovering a unipolar finite-valued signal from compressive measurements of its convolution with a known finite impulse response filter. We show that owing to the finite-value constraint the problem remains identifiable if the downsampling factor is smaller than t…
This paper considers the problem of blind deconvolution where the input signal is non-negative and sparse, and the unknown convolutional kernel is a first order autoregressive filter. Our objective is to understand if it is possible to recover both the signal and the kernel from downsampled measurem…