Super-Resolution in Automotive Pulse Radars

Delgado, Adrian Vega; Sanchez-Fernandez, Matilde; Venturino, Luca; Tulino, Antonia M.

doi:10.1109/jstsp.2021.3066126

Cited by 7 publications

(6 citation statements)

References 54 publications

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“…This yields a sparse Fourier transform of the correlation matrix, i.e., its sparse representation in the angular domain. We consider three solutions, based on the gridless approach of [17], which has been proven to provide accurate estimates even from a single or a few samples (small T ). Still, as T grows, its complexity becomes prohibitive, and we propose also two simplified approaches.…”

Section: A Mse For Correct Phase Shift Estimatesmentioning

confidence: 99%

“…Still, as T grows, its complexity becomes prohibitive, and we propose also two simplified approaches. In particular, first, we propose the gridless angle estimation (GAE) based on [17], [22]; in the second approach, denoted as subspace gridless estimation (SGE), the receive phase shifts are estimated from the LS-estimated interference correlation matrix; lastly, in the third approach, denoted gridless estimation and clustering (GEC), GAE is performed on windows of samples and the estimated phase shifts are clustered.…”

Section: A Mse For Correct Phase Shift Estimatesmentioning

confidence: 99%

“…First, we consider a solution where the AoAs are not forced to be on a grid but can take continuous values, in what is called a gridless estimation. In particular, we resort to the approach of [17], [22], which leverages the equivalence between the computation of the ℓ 0 atomic norm (AN) and a rank minimization problem restricted to the set of positive semidefinite canonical multi-level Toeplitz matrices.…”

Section: A Gridless Angle Estimationmentioning

confidence: 99%

“…Therefore, we consider its l 1 -AN relaxation to obtain a simpler problem. Let T N be the set of positive semidefinite canonical 1-Level Toeplitz matrices with dimension N (see [17] for its definition and properties). The relaxation of (29) leads to the following problem [17]…”

Section: A Gridless Angle Estimationmentioning

confidence: 99%

“…In particular, for the AoA estimation, we adopt a gridless angle estimation (GAE) approach [17], where the estimation is described as a gridless identification of several multi-dimensional frequency vectors, each corresponding to a different AoA. Although this estimator shows an improvement over the LS estimator already when using a few samples for each antenna, it becomes infeasible when applied to several samples.…”

Section: Introductionmentioning

confidence: 99%

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Estimation of Interference Correlation in mmWave Cellular Systems

Tomasin

Hasler

Tulino

et al. 2024

IEEE Trans. Wireless Commun.

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We consider a cellular network, where the uplink transmissions to a base station (BS) are interferenced by other devices, a condition that may occur, e.g., in cell-free networks or when using non-orthogonal multiple access (NOMA) techniques. Assuming that the BS treats this interference as additional noise, we focus on the problem of estimating the interference correlation matrix from received signal samples. We consider a BS equipped with multiple antennas and operating in the millimeter-wave (mmWave) bands and propose techniques exploiting the fact that channels comprise only a few reflections at these frequencies. This yields a specific structure of the interference correlation matrix that can be decomposed into three matrices, two rectangular depending on the angle of arrival (AoA) of the interference and the third square with smaller dimensions. We resort to gridless approaches to estimate the AoAs and then project the least square estimate of the interference correlation matrix into a subspace with a smaller dimension, thus reducing the estimation error. Moreover, we derive two simplified estimators, still based on the gridless angle estimation that turns out to be convenient when estimating the interference over a larger number of samples.

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Section: A Mse For Correct Phase Shift Estimatesmentioning

confidence: 99%

Section: A Mse For Correct Phase Shift Estimatesmentioning

confidence: 99%