Recently, nonuniform lineararrays (e.g., coprime/nested array) have attracted great attention of researchers in array signal processing field due to its ability to generate virtual difference coarrays. In the array d...
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Recently, nonuniform lineararrays (e.g., coprime/nested array) have attracted great attention of researchers in array signal processing field due to its ability to generate virtual difference coarrays. In the array design, a critical problem is where to place the sensors for optimal performance aiming for a maximum degree of freedom capacity and a minimum mutual coupling ratio, simultaneously. An augmented nested array concept is proposed by splitting the dense subarray of nested array into several parts, which can be rearranged at the two sides of the sparse subarray of nested array. Specifically, four closed-form expressions for the physical sensor locations and the virtual sensor locations are derived for any given element number. Compared to the (super) nested array having the same element number, the newly formed augmented nested array possesses higher degree-of-freedom capacity and less mutual coupling. In the end, numerical simulation results validate the effectiveness of the proposed arrays.
To reduce the adverse impacts of the unknown colored noise on the performance degradation of the direction-of-arrival (DOA) estimation, we propose a new gridless DOA estimation method based on fourth-order cumulant (F...
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To reduce the adverse impacts of the unknown colored noise on the performance degradation of the direction-of-arrival (DOA) estimation, we propose a new gridless DOA estimation method based on fourth-order cumulant (FOC) in this letter. We first introduce the non-redundancy single measurement vector (SMV) through FOC, which is capable of suppressing the Gaussian colored noise. Next, we analyze the distribution of the estimation error and design an estimation error tolerance scheme for it. We then combine the atomic norm minimization of the non-redundancy SMV with the above constraint scheme. This combination poses the stability of the sparsest solution. Finally, the DOA estimation is retrieved through rotational invariance techniques. Moreover, this method extends the gridless DOA estimation to the sparse linear array. Numerical simulations validate the effectiveness of the proposed method.
Downward looking sparse linear array three-dimensional synthetic aperture radar (DLSLA 3-D SAR) can obtain 3-D scene properties and has broad application prospects. However, the reconstruction of cross-track dimension...
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ISBN:
(纸本)9781509033324
Downward looking sparse linear array three-dimensional synthetic aperture radar (DLSLA 3-D SAR) can obtain 3-D scene properties and has broad application prospects. However, the reconstruction of cross-track dimension usually suffers from incomplete observation, which is caused by the non-uniformly and sparsely distributed virtual antenna phase centers. By formulating the cross-track reconstruction into the problem of sparse signal recovery, we introduce two kinds of gridless sparse recovery (GL-SR) methods to DLSLA 3-D SAR cross-track imaging, i.e., atomic norm minimization (ANM) and gridless SPICE (GLS). Compared with the conventional grid-based sparse recovery (GB-SR) methods, which assume that the scatterers are exactly on the discretized grids, the GL-SR methods can avoid the off-grid effect. Experiments compare the performance of GB-SR and GL-SR methods for DLSLA 3-D SAR cross-track reconstruction.
DOA estimation with sparse linear arrays has been extensively studied, with an emphasis on localizing more sources than sensors. A critical assumption in previous studies however is that the sources are all uncorrelat...
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ISBN:
(纸本)9781665405409
DOA estimation with sparse linear arrays has been extensively studied, with an emphasis on localizing more sources than sensors. A critical assumption in previous studies however is that the sources are all uncorrelated. In this paper, we present an algorithm that is shown to be able to localize more sources than sensors in presence of correlated or coherent sources without the knowledge of the source coherence structure. Our algorithm is generalized from our recently proposed rank-constrained ADMM approach to maximum likelihood estimation for uncorrelated sources with a uniform lineararray.
Recently, one-bit direction of arrival (DOA) estimation has received significant attention due to its low cost and low implementation complexity, while still achieving high accuracy without the need of high-resolution...
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ISBN:
(纸本)9798350377873;9798350377866
Recently, one-bit direction of arrival (DOA) estimation has received significant attention due to its low cost and low implementation complexity, while still achieving high accuracy without the need of high-resolution measurements. In this work, we consider nonlinear estimation errors under finite number of snapshots in one-bit covariance reconstruction, and propose the two-step reconstruction approach, first using the arcsine law to reconstruct the unquantized covariance matrix and then incorporating the Toeplitz Hermitian structure as prior information to reconstruct the full-scale virtual uniform lineararray (ULA) covariance matrix. It is shown that the error is smaller than the case when the two steps are swapped in order. Simulation results demonstrate the large difference in performance due to the order in which the two steps are applied, our proposed method has remarkably outperformed the current state-of-the-art solutions.
Direction augmentation (DA), followed by a subspace method such as MUSIC or ESPRIT, is a successful approach that enables localization of more uncorrelated sources than sensors with a proper sparse linear array. In th...
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ISBN:
(纸本)9781665452458
Direction augmentation (DA), followed by a subspace method such as MUSIC or ESPRIT, is a successful approach that enables localization of more uncorrelated sources than sensors with a proper sparse linear array. In this paper, we carry out a nonasymptotic performance analysis of DA-ESPRIT in the practical scenario with finitely many snapshots. We show that more uncorrelated sources than sensors are guaranteed, with overwhelming probability, to be localized using DA-ESPRIT if the number of snapshots is greater than an explicit, problem-dependent threshold. Our result does not require a fixed source separation condition, which makes it unique among existing results. Numerical results corroborating our analysis are provided.
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