Off-Grid Direction-of-Arrival Estimation Using Coprime Array Interpolation

Zhou, Chengwei; Gu, Yujie; Shi, Zhiguo; Zhang, Yimin D.

doi:10.1109/lsp.2018.2872400

Cited by 216 publications

(93 citation statements)

References 33 publications

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“…Here the minimization should be done with respect to the free parameters inΓ, i.e., γ 0 , · · · , γ K and δ i 0 , · · · , δ i K ∀i from which the WLS etimates of DoAs can be obtained through finding the roots of the polynomial (16). Akin to Section III-C, (24) can be relaxed through replacing (Γ H W −1Γ ) −1 with its consistent estimate without affecting the asymptotic behavior of the objective function [5], [31], [32], [34]. However, the reparameterized optimization problem is still complicated despite this relaxation due to the existing constraints on γ 0 , · · · , γ K and δ i 0 , · · · , δ i K ∀i.…”

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

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%

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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“…The first-order Taylor series expansion forÃ h ð Þ in formula (16) is performed 22,23 y ¼Ã þB Á Db À Á s…”

Section: R-ogomp Algorithmmentioning

confidence: 99%

Direction of arrival estimation with off-grid target based on sparse reconstruction in impulsive noise

Liang

Shi

et al. 2020

Journal of Low Frequency Noise, Vibration and Active Control

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This paper proposes a direction of arrival estimation based on sparse signal reconstruction in the presence of alpha noise by the off-grid orthogonal matching pursuit algorithm. Assuming Gaussian distribution as the noise model, the previous sparse reconstruction enhances robustness by utilizing the least square criterion-based direction of arrival estimation algorithms. However, they severely degrade, even invalid, when there is thick trailing and large impulse in the noise molded by a stable distribution. In addition, due to the discretization of the potential angle space, the accuracy of these methods will be reduced when the target is not completely on the divided mesh exactly. Increasing the grid density to improve estimation effect will increase the computation burden. The compressed sensing signal model is reconstructed by reshaping the fractional lower order covariance matrix of the sensor array received signal. Based on the reshaped signal model, the novel reshaped orthogonal matching pursuit algorithm and reshaped off-grid orthogonal matching pursuit algorithm are derived. Compared to the least square criterion method with Gaussian distribution assumption, the proposed algorithm obtains high-resolution direction of arrival estimation in a noise. Moreover, the reshaped off-grid orthogonal matching pursuit algorithm improves the direction of arrival estimation accuracy with offgrid target. Numerical simulation results demonstrate the effectiveness of proposed method in direction of arrival estimation with off-grid targets in a noise.

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“…The multiple-input and multiple-output (MIMO) technique radar can be broadly applied to the direction-of-arrival (DOA) estimation [21][22][23][24] and wide-swath imaging [25] in a big way. Further, a newer system, so-called coprime array, becomes a key technology enabling a huge number of applications, including the DOA [26,27], and adaptive beamforming [28], and the orthogonality between the subcarriers of the OFDM signal can provide frequency diversity for the waveform of these array structures. By reason that the spectral components of an OFDM signal are orthogonal to each other, multiple packets of data can be sent simultaneously without mutual interference, each spectral component as a carrier transporting its corresponding data packet in time and frequency domains.…”

Section: Introductionmentioning

confidence: 99%

ECCM Schemes against Deception Jamming Using OFDM Radar with Low Global PAPR

Wang

Zhang

Wang

et al. 2020

Sensors

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In this paper, a type of effective electronic counter-countermeasures (ECCM) technique for suppressing the high-power deception jamming using an orthogonal frequency division multiplexing (OFDM) radar is proposed. Concerning the velocity deception jamming, the initial phases of the pulses transmitted in a coherent processing interval (CPI) are designed to minimize the jamming power within a specific range, forming a notch around the jamming in the Doppler spectrum. For the purpose of suppressing the range deception jamming and the joint range-velocity deception jamming, the phase codes of the subcarriers belonging to the OFDM pulses are optimized to minimize the jamming power, distributing some specific bands in the range and the range-velocity domain, respectively. According to Parseval's theorem, the phase encoding, acting as the coding manner of the OFDM subcarriers can ensure that the energy of each OFDM symbol stays the same. It is worth noticing that the phase codes of the OFDM subcarriers can influence the peak-to-average power ratio (PAPR). Thus, an optimization problem is formulated to optimize the phase codes of the subcarriers under the constraint of global PAPR, which can regulate the PAPRs of multiple OFDM symbols at the same time. The proposed problem is non-convex; therefore, it is a huge challenge to tackle. Then we present a method named by the phase-only alternating direction method multipliers (POADMM) to solve the aforementioned optimization problem. Some necessary simulation results are provided to demonstrate the effectiveness of the proposed radar signaling strategy 4 of 26 norm. In addition, arg{x} denotes the phase vector of x.|·|, {·} and {·} are the magnitude, real part, and image part of a complex-valued input. The operators ⊕ and ⊗ are the Hadamard operator and Kronecker product operator, respectively. Let I, 1, and 0 denote the identity matrix, matrix of 1 and zero matrix (their size is determined from the context). The operators, Diag(·) and diag(·), are, respectively, the diagonal matrices formed by the entries of the vector argument and the column vector containing the principal diagonal of the matrix argument. denotes the gradient operator and vec(·) represents the vectorization of an input matrix.

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Off-Grid Direction-of-Arrival Estimation Using Coprime Array Interpolation

Cited by 216 publications

References 33 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

Direction of arrival estimation with off-grid target based on sparse reconstruction in impulsive noise

ECCM Schemes against Deception Jamming Using OFDM Radar with Low Global PAPR

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