18 accepted papers
Wireless channel modeling is essential for the design, analysis, and optimization of modern wireless sensing and communication systems. However, accurately modeling wireless channels in electrically large and complex environments remains a long-standing challenge, owing to the intricate interactions…
We consider the problem of adaptive sensing for multi-path channel estimation in millimeter-wave (mmWave) communications system with single RF chain. Current adaptive sensing approaches either focus on estimating the single dominant path or assume apriori knowledge of the number of multi-path compon…
We address the challenge of achieving angular super-resolution in multi-antenna radar systems that are widely used for localization, navigation, and automotive perception. A multi-antenna radar achieves very high resolution by computationally creating a large virtual sensing system using very few ph…
Sparse linear arrays (SLAs) have gained significant attention for automotive radar due to their enhanced aperture and angular resolution. A promising approach to Direction-of-Arrival (DOA) estimation with only a single temporal snapshot and SLAs is to interpolate the missing measurements by solving…
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 concerns understanding the fundamental performance benefits of co-array based super-resolution techniques in low SNR settings. Proper sensor placement in the form of a sparse nested array allows the generation of difference co-arrays with large virtual apertures that offer higher resoluti…
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…
This paper considers the problem of lower bounding the mean-squared-error (MSE) of unbiased super-resolution estimates. In literature, only upper bounds on the MSE are available which scale with the so-called super-resolution factor (SRF). However, the upper bound does not indicate whether the MSE i…
This paper considers the problem of super-resolution reconstruction by casting it as an optimization problem with positive constraints and non-convex objective function. Enforcing the solution to be simultaneously sparse and non-negative naturally leads to a non-convex l <sub xmlns:mml="http://www.w…
This paper considers the problem of sparse support recovery in Multiple Measurement Vector (MMV) models, where the support size (K) can exceed the dimension (M) of individual measurement vectors. Existing results in this regime mostly establish asymptotic performance guarantees, where the number of…
This paper considers the problem of super-resolution with positive constraints. By utilizing the concept of Modulus of Continuity (MC), we propose a unified framework for analyzing the robustness of super-resolution reconstruction in presence of noise, which is algorithm-independent and emphasizes t…
This paper analyzes the Cramér-Rao Bound associated with the estimation of certain sparse hyper-parameters in the Sparse Bayesian Learning (SBL) framework, that crucially control the sparsity of the desired signal. The CRB is shown to exhibit saturation with respect to the number of measurements, i.…
This paper considers the problem of co-array interpolation for direction-of-arrival (DOA) estimation with sparse nonuniform arrays. By utilizing the much longer difference co-array associated with these arrays, it is possible to perform DOA estimation of more sources than sensors. Since the co-array…
The problem of sparse phase retrieval is considered where the goal is to recover a sparse complex valued vector (with s non zero elements) from the magnitudes of its linear measurements. Using a modified and partially randomized version of a newly proposed structured sampler, namely the Partial Nest…