Unified analysis of co-array interpolation for direction-of-arrival estimation

Qiao, Heng; Pal, Piya

doi:10.1109/icassp.2017.7952718

Cited by 48 publications

(16 citation statements)

References 16 publications

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“…4(b), the proposed algorithm resolves both sources in their true directions with shape peaks. In the following, the root mean square error (RMSE) of the estimated DoAs obtained from the proposed algorithm is compared with the Cramér-Rao bound (CRB) [20] and those achieved by the state-of-the-art DoA estimation algorithms, including the sparse signal reconstruction (SSR) algorithm [2], the NNM algorithm [12], the NNM with PSD constraint (NUC-PSD) algorithm [14], the maximum entropy (ME) algorithm [14], the ANM algorithm [15], and the covariance matrix sparse reconstruction (CMSR) algorithm [4]. The direction of the incident signal is randomly generated from the Gaussian distribution N (0 • , (1 • ) 2 ), and the results are computed using 1, 000 Monte Carlo trials.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…As for (14), there is no closed-form solution but its can be solved efficiently by using available SDP solvers (e.g., SDPT3, SeDuMi). As a result, the minimization problem ( 13) can be efficiently solved within a few iterations.…”

Section: Doa Estimation Via Toeplitz Matrix Reconstructionmentioning

confidence: 99%

“…The other proposes to fill the holes via interpolation, e.g., a gridless DoA estimation algorithm based on nuclear norm minimization (NNM) [12], [13]. Later, the positive semi-definite (PSD) structure of the covariance matrix [14] is exploited to design the nuclear norm for covariance matrix construction. More recently, a virtual array interpolation algorithm based on the atomic norm minimization (ANM) [15] is presented to recover missing array data in a gridless manner.…”

Section: Introductionmentioning

confidence: 99%

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Rank Minimization-based Toeplitz Reconstruction for DoA Estimation Using Coprime Array

Liu,

Mao,

Zhang

et al. 2021

Preprint

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In this paper, we address the problem of direction finding using coprime array, which is one of the most preferred sparse array configurations. Motivated by the fact that nonuniform element spacing hinders full utilization of the underlying information in the receive signals, we propose a directionof-arrival (DoA) estimation algorithm based on low-rank reconstruction of the Toeplitz covariance matrix. The atomicnorm representation of the measurements from the interpolated virtual array is considered, and the equivalent dual-variable rank minimization problem is formulated and solved using a cyclic optimization approach. The recovered covariance matrix enables the application of conventional subspace-based spectral estimation algorithms, such as MUSIC, to achieve enhanced DoA estimation performance. The estimation performance of the proposed approach, in terms of the degrees-of-freedom and spatial resolution, is examined. We also show the superiority of the proposed method over the competitive approaches in the root-mean-square error sense.

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Section: Simulation Resultsmentioning

confidence: 99%

Section: Doa Estimation Via Toeplitz Matrix Reconstructionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Rank Minimization-based Toeplitz Reconstruction for DoA Estimation Using Coprime Array

Liu,

Mao,

Zhang

et al. 2021

Preprint

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“…In space and air communication, the relative highspeed motion of the transceivers increases the complexity of beam alignment. Therefore, the accurate direction of arrival (DoA) and direction of departure (DoD) estimation is a key issue in MIMO communication [90][91][92]. Solutions have been proposed not only through the implementation of estimation algorithms such as Multiple Signal Classification (MU-SIC) and Estimation of Signal Parameters via Rational Invariance Techniques (ESPRIT), the implemen- tations of customized circuit blocks can also improve the speed and accuracy of alignment significantly [93].…”

Section: Space and Satellite Communicationmentioning

confidence: 99%

A review on applications of integrated terahertz systems

Venkatesh

Saeidi

2021

China Commun.

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A review on Terahertz end-to-end systems with an emphasis on integrated approaches is presented. Four major catalogs of THz integrated systems, including THz communication systems, THz imaging systems, THz radars, and THz spectroscopy systems, are reviewed in this article. The performance of integrated systems is compared with non-integrated solutions, followed by a discussion on the trend in future research avenues and applications.

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“…In a seminal paper [5], the authors unified the notion of aperture synthesis for different types of coherent (usually active) and incoherent (usually passive) imaging techniques under the common idea of virtual sum and difference coarrays. In recent times, sparse arrays have emerged as a powerful way to systematically generate large sum and difference co-arrays, and perform source localization and parameter estimation with increased degrees of freedom [6][7][8][9][10][11][12][13]. Ideas behind correlation-based virtual processing are also integral to optical imaging techniques such as correlation microscopy [14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Fundamental Trade-Offs in Noisy Super-Resolution with Synthetic Apertures

Shahsavari

Millhiser

Pal

2021

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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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 resolution. However, the performance of super-resolution aperture synthesis techniques is known to degrade in presence of large noise levels. The main contribution of this paper is to rigorously establish that nested arrays provide lower Cramér-Rao bounds than a ULA (with the same number of sensors) in the low SNR regime, and therefore can lead to better resolvability of closely spaced sources. Numerical experiments are performed to validate theoretical claims, including demonstration of MUSIC spectra with two closely-spaced sources using ULA and nested arrays in various SNR settings. 1

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Unified analysis of co-array interpolation for direction-of-arrival estimation

Cited by 48 publications

References 16 publications

Rank Minimization-based Toeplitz Reconstruction for DoA Estimation Using Coprime Array

Rank Minimization-based Toeplitz Reconstruction for DoA Estimation Using Coprime Array

A review on applications of integrated terahertz systems

Fundamental Trade-Offs in Noisy Super-Resolution with Synthetic Apertures

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