Direction-of-Arrival Estimation for Coprime Array via Virtual Array Interpolation

Zhou, Chengwei; Gu, Yujie; Fan, Xing; Shi, Zhiguo; Mao, Guoqiang; Zhang, Yimin D.

doi:10.1109/tsp.2018.2872012

Cited by 470 publications

(226 citation statements)

References 46 publications

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“…This parametrizes the optimization problem only in terms of the desired parameters, i.e., γ 0 , · · · , γ K and thereby eliminates the constraints corresponding to δ i 0 , · · · , δ i K ∀i. Towards this, in what follows, we use the fact that the remainder h i (z) generated by division of q i (z) by f (z) is zero according to (23). Letñ be an integer such that a ĩ n < K and a ĩ n+1 ≥ K. It is then possible to rewrite…”

Section: Wls Implementation For Slas With Holes In Co-arraysmentioning

confidence: 99%

“…Further, CAB-MUSIC and CAB-ESPRIT suffer from an inherent performance loss when applied to SLAs with holes in their difference co-arrays, such as co-prime arrays, since they discard the information contained in the non-contiguous segment of the difference co-array. To avoid this performance loss, array interpolation-based algorithms has been used for co-prime arrays in [23]- [25] where a convex optimization problem is formulated to recover the covariance matrix of a virtual ULA interpolated from the co-prime array. The performance of interpolation-based algorithms has been analyzed in [26].…”

Section: Introductionmentioning

confidence: 99%

“…In addition, the authors of this paper have recently proposed a least squares estimator capable of exploiting the information contained in the non-contiguous ULA segment of the difference co-array [27]. Although the proposed estimators in [23], [24], [27] succeed in dealing with the performance loss due to omission of the non-contiguous segment of difference co-array, they are still unable to achieve the CRB performance. It is known that the covariance matrix of uncorrelated signals received by a linear array is structured, e.g, Hermitian Toeplitz for ULA [28].…”

Section: Introductionmentioning

confidence: 99%

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An Asymptotically Efficient Weighted Least Squares Estimator for Co-Array-Based DoA Estimation

Sedighi

Shankar

Ottersten

2020

IEEE Trans. Signal Process.

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Co-array-based Direction of Arrival (DoA) estimation using Sparse Linear Arrays (SLAs) has recently gained considerable interest in array processing thanks to its capability of providing enhanced degrees of freedom. Although the literature presents a variety of estimators in this context, none of them are proven to be statistically efficient. This work introduces a novel estimator for the co-array-based DoA estimation employing the Weighted Least Squares (WLS) method. An analytical expression for the large sample performance of the proposed estimator is derived. Then, an optimal weighting is obtained so that the asymptotic performance of the proposed WLS estimator coincides with the Cramér-Rao Bound (CRB), thereby ensuring asymptotic statistical efficiency of resulting WLS estimator. This implies that the proposed WLS estimator has a significantly better performance compared to existing methods. Numerical simulations are provided to validate the analytical derivations and corroborate the improved performance.Index Terms-Sparse linear arrays, direction of arrival (DoA) estimation, weighted least squares estimator, Cramér-Rao bound (CRB), performance analysis, asymptotically statistically efficient estimator.

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Section: Wls Implementation For Slas With Holes In Co-arraysmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

An Asymptotically Efficient Weighted Least Squares Estimator for Co-Array-Based DoA Estimation

Sedighi

Shankar

Ottersten

2020

IEEE Trans. Signal Process.

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“…erefore, the fast-moving targets are defined as the ones with nonstationary DOA between adjacent pulses. e compressed sensing-(CS-) based methods have been proposed [29,30] to improve the DOA estimation performance with fewer measurements, where the target sparsity is exploited in the spatial domain [31][32][33][34][35]. erefore, the DOA estimation for the sparse targets is transformed into the sparse reconstruction problem in the CS-based methods [36,37,37], and the sparse-based methods can be used in the scenario with fast-moving targets.…”

Section: Introductionmentioning

confidence: 99%

A Super-Resolution DOA Estimation Method for Fast-Moving Targets in MIMO Radar

Liu

Tang

Bai

et al. 2020

Mathematical Problems in Engineering

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Direction of arrival (DOA) estimation is an essential problem in the radar systems. In this paper, the problem of DOA estimation is addressed in the multiple-input and multiple-output (MIMO) radar system for the fast-moving targets. A virtual aperture is provided by orthogonal waveforms in the MIMO radar to improve the DOA estimation performance. Different from the existing methods, we consider the DOA estimation method with only one snapshot for the fast-moving targets and achieve the superresolution estimation from the snapshot. Based on a least absolute shrinkage and selection operator (LASSO), a denoise method is formulated to obtain a sparse approximation to the received signals, where the sparsity is measured by a new type of atomic norm for the MIMO radar system. However, the denoise problem cannot be solved efficiently. en, by deriving the dual norm of the new atomic norm, a semidefinite matrix is constructed from the denoise problem to formulate a semidefinite problem with the dual optimization problem. Finally, the DOA is estimated by peak-searching the spatial spectrum. Simulation results show that the proposed method achieves better performance of the DOA estimation in the MIMO radar system with only one snapshot.(DFT) [19] is used to estimate the DOA, where the received signals are sampled by the antennas in the spatial domain, and then the DOA estimation is equal to a corresponding frequency estimation in the transformed domain. erefore, the frequency (DOA) in the spatial domain is obtained by the DFT methods, but the resolution of DFT method is limited by Rayleigh criterion. e methods that can break through the Rayleigh criterion are called super-resolution methods. Multiple signal classification (MUSIC) method [20][21][22], Root-MUSIC [23], and the estimation of signal parameters via rotational invariant techniques (ESPRIT) method [24][25][26] are three most essential super-resolution methods. e noise subspace and signal subspace are obtained in the MUSIC and ESPRIT methods to estimate the DOA, respectively. A TOD-MUSIC algorithm is proposed in [27] to estimate the DOA in the scenario with low signal-to-noise ratio (SNR) with diversity bistatic MIMO radar.However, the subspaces are obtained from the estimated covariance matrix of the received signals, so the multiple measurements are needed in MUSIC and ESPRIT methods to achieve a reasonable estimation of the covariance matrix.

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“…Many sparse signal recovery algorithms have been proposed in CS literature, which can be applied to estimate the DOAs by exploiting the spatial sparsity [20][21][22][23][24]. FOCUSS (FOcal Underdetermined System Solver) [25] uses an iterative method which is based on weighted norm minimization.…”

Section: Introductionmentioning

confidence: 99%

Grid Reconfiguration Method for Off-Grid DOA Estimation

Ling

Gao

et al. 2019

Electronics

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Off-grid algorithms for direction of arrival (DOA) estimation have become attractive because of their advantages in resolution and efficiency over conventional ones. In this paper, we propose a grid reconfiguration direction of arrival (GRDOA) estimation method based on sparse Bayesian learning. Unlike other off-grid methods, the grid points of GRDOA are treated as dynamic parameters. The number and position of the grid points are varied iteratively via a root method and a fission process. Then, the grid gets reconfigured through some criteria. By iteratively updating the reconfigured grid, DOAs are estimated completely. Since GRDOA has fewer grid points, it has better computational efficiency than the previous methods. Moreover, GRDOA can achieve better resolution and relatively higher accuracy. Numerical simulation results validate the effectiveness of GRDOA.

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Direction-of-Arrival Estimation for Coprime Array via Virtual Array Interpolation

Cited by 470 publications

References 46 publications

An Asymptotically Efficient Weighted Least Squares Estimator for Co-Array-Based DoA Estimation

An Asymptotically Efficient Weighted Least Squares Estimator for Co-Array-Based DoA Estimation

A Super-Resolution DOA Estimation Method for Fast-Moving Targets in MIMO Radar

Grid Reconfiguration Method for Off-Grid DOA Estimation

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