Fast detection of coherent signals using pre-conditioned root-MUSIC based on Toeplitz matrix reconstruction

Goian, A.; AlHajri, Mohamed I.; Shubair, Raed M.; Weruaga, Luis; Kulaib, A. R.; AlMemari, R.; Darweesh, Muna

doi:10.1109/wimob.2015.7347957

Cited by 20 publications

(16 citation statements)

References 15 publications

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“…By using the SVD decomposition of R Z , we have

{boldR}_{boldZ} = {boldU}_{boldZ} 𝚺_{boldZ} {boldV}_{boldZ}^{H} + {boldU}_{boldn} 𝚺_{boldn} {boldV}_{boldn}^{H},

where U Z and V Z contain the K n + P dominant left and right singular vectors, respectively; also, Σ Z consists of the K n + P largest singular values. As shown in the works of Chen et al, Goian et al, and Zhang and Xu, the Toeplitz matrix can overcome the rank deficiency problem of the received signals covariance matrix resulting from the coherency between the signals. Regarding

{false[{\overset{boldu}{true}}_{boldZ k} false]}_{m}

which is the m th element of the k th column of matrix

{\overset{boldU}{true}}_{boldZ} = false[{\overset{boldu}{true}}_{boldZ 1}, {\overset{boldu}{true}}_{boldZ 2}, …, {\overset{boldu}{true}}_{boldZ K_{n} + P} false]

, we can express

{false[{\overset{boldu}{true}}_{boldZ k} false]}_{m}

{false[{\overset{boldu}{true}}_{boldZ k} false]}_{m} = true \sum_{i = 1}^{K_{n}} ζ_{i} e^{j \frac{true}{2 π d_{y} m \cos false(α_{i} false)}} + true \sum_{p = 1}^{P} true \sum_{i = 1}^{K_{p}}

…”

Section: Proposed Algorithmmentioning

confidence: 99%

“…The problem of two‐dimensional (2D) direction‐of‐arrivals (DOAs) estimation of a mixture of noncoherent and coherent narrowband signals has attracted a lot of interest in many practical application scenarios such as radar, sonar, and wireless communication. Compared with the maximum likelihood (ML) method in the works of Sheinvald et al and Stoica and Nehorai, the subspace‐based algorithms (eg, other works) can offer a good trade‐off between the performance and computational complexity and have attained considerable attentions. The state‐of‐the‐art algorithms in this category are multiple signal classification (MUSIC) and estimation of signal parameters via rotation invariance techniques (ESPRIT) .…”

Section: Introductionmentioning

confidence: 99%

“…Compared with the maximum likelihood (ML) method in the works of Sheinvald et al and Stoica and Nehorai, the subspace‐based algorithms (eg, other works) can offer a good trade‐off between the performance and computational complexity and have attained considerable attentions. The state‐of‐the‐art algorithms in this category are multiple signal classification (MUSIC) and estimation of signal parameters via rotation invariance techniques (ESPRIT) . Since the coherent signals cause rank deficiency of the received signal covariance matrix, most of these algorithms cannot be directly employed to estimate the DOA of coherent signals.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Two‐dimensional DOA estimation for noncoherent and coherent signals using subspace approach

Shahimaeen

Dehghani

Dodangeh

2019

Trans Emerging Tel Tech

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This paper focuses on the problem of two-dimensional (2D) direction-of-arrival (DOA) estimation for a mixture of noncoherent and coherent signals impinging on two parallel uniform linear arrays. It adopts the principal-eigenvector utilization for modal analysis (PUMA)-based algorithm. The algorithm offers a closed-form solution for updating the 2D DOA estimation and pairs the estimated angles automatically as well as has mild computational complexity. Finally, simulation results confirm that our PUMA-based algorithm has good performance in handling 2D DOA estimation even in the presence of coherent signals.Trans Emerging Tel Tech. 2019;30:e3752.wileyonlinelibrary.com/journal/ett

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“…By using the SVD decomposition of R Z , we have

{boldR}_{boldZ} = {boldU}_{boldZ} 𝚺_{boldZ} {boldV}_{boldZ}^{H} + {boldU}_{boldn} 𝚺_{boldn} {boldV}_{boldn}^{H},

{false[{\overset{boldu}{true}}_{boldZ k} false]}_{m}

which is the m th element of the k th column of matrix

{\overset{boldU}{true}}_{boldZ} = false[{\overset{boldu}{true}}_{boldZ 1}, {\overset{boldu}{true}}_{boldZ 2}, …, {\overset{boldu}{true}}_{boldZ K_{n} + P} false]

, we can express

{false[{\overset{boldu}{true}}_{boldZ k} false]}_{m}

{false[{\overset{boldu}{true}}_{boldZ k} false]}_{m} = true \sum_{i = 1}^{K_{n}} ζ_{i} e^{j \frac{true}{2 π d_{y} m \cos false(α_{i} false)}} + true \sum_{p = 1}^{P} true \sum_{i = 1}^{K_{p}}

…”

Section: Proposed Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Two‐dimensional DOA estimation for noncoherent and coherent signals using subspace approach

Shahimaeen

Dehghani

Dodangeh

2019

Trans Emerging Tel Tech

View full text Add to dashboard Cite

show abstract

“…To obtain the off-grid estimation for fair comparison, several Root-MUSIC 22 based algorithms were used for comparison with the proposed method. Since the echoes were coherent, forward spatial smoothingbased Root-MUSIC (FSS-RM), 23 forward and backward spatial smoothing-based Root-MUSIC (FBSS-RM), 24 and Toeplitz reconstruction-based Root-MUSIC (TR-RM) 25,26 were utilized for comparison. In addition, based on coprime array and MCA, all the above methods were used for comparison to illustrate validity of resolving phase ambiguity.…”

Section: Simulationmentioning

confidence: 99%

Beamforming interferometry bathymetry method based on multi-coprime sensor array

Zhou

Shen

Jian-jun

et al. 2019

International Journal of Distributed Sensor Networks

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Recently, coprime array has been a popular research field in the application of direction-of-arrival estimation. Compared with uniform linear array, coprime array can be used to expand array aperture with fewer sensors, and it also has a nice direction-of-arrival estimation performance. According to coprime property, the direction of arrival can be obtained by intersecting the candidate estimation sets from several subarrays. However, when the directions of multiple sources meet a particular relation, the unambiguous phase cannot be unwrapped through the coprime array. In this article, a multi-coprime array is proposed to address the problem, utilizing about half number of array elements and reducing hardware complexity compared with uniform linear array. Then, a low-complexity beamforming interferometry algorithm via multi-coprime array is proposed to reduce computational complexity. Numerical results, including simulation and actual data processing, are provided to indicate that the processing on multi-coprime array can successfully resolve phase ambiguity. Specially, when signal-to-noise ratio exceeds about 24 dB and the element number of subarray in multi-coprime array exceeds 15 for the array geometry in this article, the proposed method achieves close estimation performance with fewer sensors compared with uniform linear array.

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“…According to previous research studies, these approaches have been proved to play an important role in eliminating the rank loss of the covariance matrix, but they are not flawless because an unignorable large aperture loss appears under these approaches as well, degrading the accuracy of parameter estimation. Apart from these approaches, the approaches of parameter estimation for coherent signals based on the Toeplitz matrix signal model have received considerable attention and developments because the rank of the Toeplitz matrix is only related to the DOA of signals and cannot be affected by the coherency between them [6][7][8][9][10]. In addition, in the context of underwater environment and target imaging, the coherent signal analysis methods proposed in [11,12] are also worthy of attention.…”

Section: Introductionmentioning

confidence: 99%

Coherent Signal Parameter Estimation by Exploiting Decomposition of Tensors

Liu

Wang

et al. 2019

Mathematical Problems in Engineering

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The problem of parameter estimation of coherent signals impinging on an array with vector sensors is considered from a new perspective by means of the decomposition of tensors. Signal parameters to be estimated include the direction of arrival (DOA) and the state of polarization. In this paper, mild deterministic conditions are used for canonical polyadic decomposition (CPD) of the tensor-based signal model; i.e., the factor matrices can be recovered, as long as the matrices satisfy the requirement that at least one is full column rank. In conjoint with the estimation of signal parameters via the algebraic method, the DOAs and polarization parameters of coherent signals can be resolved by virtue of the first and second factor matrices. Hereinto, the key innovation of the proposed approach is that the proposed approach can effectively estimate the coherent signal parameters without sacrificing the array aperture. The superiority of the proposed algorithm is shown by comparing with the algorithms based on higher order singular value decomposition (HOSVD) and Toeplitz matrix. Theoretical and numerical simulations demonstrate the effectiveness of the proposed approach.

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Fast detection of coherent signals using pre-conditioned root-MUSIC based on Toeplitz matrix reconstruction

Cited by 20 publications

References 15 publications

Two‐dimensional DOA estimation for noncoherent and coherent signals using subspace approach

Two‐dimensional DOA estimation for noncoherent and coherent signals using subspace approach

Beamforming interferometry bathymetry method based on multi-coprime sensor array

Coherent Signal Parameter Estimation by Exploiting Decomposition of Tensors

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