A simultaneous sparse approximation approach for DOA estimation

Hosseini, Seyyed Moosa; Sadeghzadeh, R. A.; Azizipour, Mohammad Javad

doi:10.1109/spices.2015.7091486

Cited by 3 publications

(5 citation statements)

References 12 publications

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“…Direction of arrival estimation is a basic problem in signal processing. Due to the spatial sparsity of source signals, many compressive sensing type algorithms are proposed, such as [20,24,27]. In this part, we compare the performances of our TLSOMP algorithm, the SOMP method [38], the Landweber iteration method for ℓ 1 -optimization [10], the simultaneous sparse Bayesian learning method [44], and the baseline LS method on solving the DOA estimation problem.…”

Section: Challenging Examples About Direction Of Arrival (Doa) Estima...mentioning

confidence: 99%

A Two-Level Simultaneous Orthogonal Matching Pursuit Algorithm for Simultaneous Sparse Approximation Problems

Shao,

Zhang,

null

et al. 2023

CSIAM-AM

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In this paper, we propose a two-level simultaneous orthogonal matching pursuit (TLSOMP) algorithm for simultaneous sparse approximation (SSA) problems. Most existing algorithms for SSA problems are directly generalized from the ones for the sparse approximation (SA) problems, for example, the simultaneous orthogonal matching pursuit (SOMP) method is generalized from the orthogonal matching pursuit (OMP) method. Our newly proposed algorithm is designed from another viewpoint. We first analyze the noiseless case and propose a selection algorithm. Motivated by the analysis and presuming noise as a perturbation, we extend the selection algorithm into a TLSOMP algorithm. This novel algorithm mainly uses the information from the subspace spanned by the multiple signals, which is not available in SA problems. Numerical experiments show the superiority of our TLSOMP algorithm over other traditional SSA solvers.

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Section: Challenging Examples About Direction Of Arrival (Doa) Estima...mentioning

confidence: 99%

A Two-Level Simultaneous Orthogonal Matching Pursuit Algorithm for Simultaneous Sparse Approximation Problems

Shao,

Zhang,

null

et al. 2023

CSIAM-AM

View full text Add to dashboard Cite

show abstract

“…As a result, the BS could distinguish between nearby and faraway users in the cell by observing the

θ_{k, m}

’s received from all M paths and comparing them with the threshold

θ_{th}

if it can estimate the related angles somehow. Fortunately, there are many efficient approaches for AoA estimation in the literature [37–40]. In the subspace family, the multiple signal classification (MUSIC) and estimation of signal parameters via rational invariance technique (ESPRIT) are two well‐known AoA estimation algorithms [40].…”

Section: User Clusteringmentioning

confidence: 99%

“…In the subspace family, the multiple signal classification (MUSIC) and estimation of signal parameters via rational invariance technique (ESPRIT) are two well‐known AoA estimation algorithms [40]. Furthermore, when the number of transmit antennas is sufficiently larger than the number of users, compressed sensing algorithms are employed to achieve high resolution in the angle estimation [37, 38]. With regard to the massive MIMO scenario, where we have

N ≫ K

, the BS side would not face a serious challenge for the true or mean AoAs estimation.…”

Section: User Clusteringmentioning

confidence: 99%

Channel estimation for FDD multi‐user massive MIMO systems: a greedy approach based on user clustering

Azizipour

Mohamed-pour

2019

IET signal process.

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Obtaining channel state information (CSI) at the transmitter side is essential to take advantage of spectral and energy efficiency in massive multiple-input multiple-output systems. In particular, due to a large number of antennas at the base station (BS) side, the required pilots for downlink channel estimation and the following CSI feedback in the uplink path would be prohibitively large when the system employs frequency-division duplex protocol for data transmission. In this study, the authors propose a novel compressed sensing algorithm which exploits the commonly shared sparsity among nearby users to reduce the estimation error and the number of assigned pilots accordingly. Moreover, to gather users' channel with common sparsity, a clustering procedure is introduced, which groups active users located in the cell according to their mean angle of arrivals received by the uplink signals at the BS antenna array. After clustering users properly, the proposed algorithm can exploit shared support set existing in each cluster efficiently, thereby improving CSI estimation performance. Numerical results demonstrate that the clustering idea along with the proposed algorithm, outperforms other solutions, and is capable of approaching the performance bound when the transmit power is increased.

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“…In this work, we exploit the idea of using SVD decomposition to compute signal subspace and eliminate additive noise. This approach has been combined with an iterative approach to reconstruct row sparse matrix [15]. For simplicity, we first consider the noiseless case described in Eq.…”

Section: Proposed Algorithmmentioning

confidence: 99%

“…The second experiment concerns the effect of aperture length on DOA estimation in NLA scenario. We compare ULA 15 to four NLAs with the same number of active sensors. The experiment has been carried as a Monte Carlo simulation with three randomly chosen angles in each of the iterations.…”

Section: Nonuniform Linear Arraymentioning

confidence: 99%

DOA estimation using multiple measurement vector model with sparse solutions in linear array scenarios

Hosseini

Sadeghzadeh

Virdee

2017

J Wireless Com Network

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A novel algorithm is presented based on sparse multiple measurement vector (MMV) model for direction of arrival (DOA) estimation of far-field narrowband sources. The algorithm exploits singular value decomposition denoising to enhance the reconstruction process. The proposed multiple nature of MMV model enables the simultaneous processing of several data snapshots to obtain greater accuracy in the DOA estimation. The DOA problem is addressed in both uniform linear array (ULA) and nonuniform linear array (NLA) scenarios. Superior performance is demonstrated in terms of root mean square error and running time of the proposed method when compared with conventional compressed sensing methods such as simultaneous orthogonal matching pursuit (S-OMP), l 2,1 minimization, and root-MUISC.

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A simultaneous sparse approximation approach for DOA estimation

Cited by 3 publications

References 12 publications

A Two-Level Simultaneous Orthogonal Matching Pursuit Algorithm for Simultaneous Sparse Approximation Problems

A Two-Level Simultaneous Orthogonal Matching Pursuit Algorithm for Simultaneous Sparse Approximation Problems

Channel estimation for FDD multi‐user massive MIMO systems: a greedy approach based on user clustering

DOA estimation using multiple measurement vector model with sparse solutions in linear array scenarios

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