DOA estimation via shift-invariant matrix completion

Garg, Vaibhav; Giménez-Febrer, Pere; Pagès-Zamora, Alba; Santamarı́a, Ignacio

doi:10.1016/j.sigpro.2021.107993

Cited by 8 publications

(13 citation statements)

References 26 publications

(43 reference statements)

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“…Among these algorithms, compressed sensing (CS) is a new signal processing theory in recent years; since it was born, the relevant research has been carried out continuously. It can be classified into grid-division and grid-less methods; the first needs to par-tition the airspace into multiple grids before signal recovery [37][38][39], while the idea of grid-less originally presented by Candes and Fernandez [40] can solve the DOA in continuous domain; as a result, it has aroused great interests and attentions [41][42][43], but the problem of positive semidefinite problem is inevitable, so the calculation is heavy.…”

Section: Introductionmentioning

confidence: 99%

Accessorial Locating for Internet of Vehicles Based on DOA Estimation in Industrial Transportation

Zhen

Guo

2021

Wireless Communications and Mobile Computing

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With the improvement of living standard and the development of science and technology, Internet of Vehicle (IOV) will play an important part in industrial transportation as a main research field of Internet of Things. As a result, it is very necessary to grasp the location of vehicle. However, the traditional single global position system is easily affected by the external environment, so an accessorial locating approach based on wideband direction of arrival (DOA) estimation in intelligent transportation is proposed. First, model the array received signal on the road infrastructure. Then, by means of random forest regression (RFR) in the supervised learning, upper triangle elements of the covariance matrix of each frequency and the actual DOA are, respectively, extracted as the input features and output parameters; thus, the corresponding prediction coefficients are solved by training. After that, the trained RFR model can be used to calculate the final direction using test samples. Finally, these vehicles can be located according to the geometrical relation between the vehicle and the infrastructure. The proposed algorithm is not only suitable for uncorrelated signals but also for uncorrelated and correlated mixed signals without wideband focusing. The simulations show that compared with some sparse recovery algorithm, the prediction accuracy and resolution are effectively improved.

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Section: Introductionmentioning

confidence: 99%

Accessorial Locating for Internet of Vehicles Based on DOA Estimation in Industrial Transportation

Zhen

Guo

2021

Wireless Communications and Mobile Computing

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“…A data matrix with missing entries might occur when i) one or more sensors are damaged, or ii) only a few sensors are intentionally sampled to reduce the overall hardware cost. The latter is the case when using a multi-switch antenna selection architecture [10], [11] in which, at every time instant, a random switch selects a subset of antennas whose RF signals are downconverted and further processed. The data matrix with multi-switch antenna selection has therefore missing entries.…”

Section: Introductionmentioning

confidence: 99%

“…The data matrix with multi-switch antenna selection has therefore missing entries. A low-rank matrix completion (MC) approach [12] that exploits the shift-invariance property (SIP) of ULAs has recently been proposed in [11] to recover the complete data matrix as if it had been received by the full array. Once the data matrix has been reconstructed, the direction-of-arrival (DOA) of the received signals can readily be estimated.…”

Section: Introductionmentioning

confidence: 99%

“…A similar idea has been applied in [13] for multiple-input and multiple-output (MIMO) radar. The shift-invariant matrix completion (SIMC) method proposed in [11], however, assumes that the number of sources is known.…”

Section: Introductionmentioning

confidence: 99%

“…Motivated by this result, we propose a subspace-based order estimation criterion, which exploits the shift-invariance property of ULAs. The proposed approach uses the SIMC algorithm [11] for increasing values of dimension and the order is estimated by evaluating chordal subspace distance (CSD) [16] between two submatrices extracted from the reconstructed matrix.…”

Section: Introductionmentioning

confidence: 99%

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Order Estimation via Matrix Completion for Multi-Switch Antenna Selection

Garg¹,

Pagès-Zamora²,

Santamarı́a³

2021

IEEE Signal Process. Lett.

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This letter addresses the problem of order estimation for uniform linear arrays (ULAs) with multi-switch antenna selection in the small-sample regime. Multi-switch antenna selection results in a data matrix with missing entries, a scenario for which existing order estimation methods that build on the eigenvalues of the sample covariance matrix do not perform well. A direct application of the Davis-Kahan theorem allows us to show that the signal subspace is quite robust in the presence of missing entries. Based on this finding, this letter proposes a matrix completion (MC) subspace-based order estimation criterion that exploits the shift-invariance property of ULAs. A recently proposed shift-invariant matrix completion (SIMC) method is used for reconstructing the data matrix, and the proposed order estimation criterion is based on the chordal subspace distance between two submatrices extracted from the reconstructed matrix for increasing values of the dimension of the signal subspace. Our simulation results show that the method provides accurate order estimates with percentages of missing entries higher than 50%.

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