Recent advancements in array signal processing focus on enhancing source detection and reducing the effects of mutual coupling among array elements. This has been achieved using Direction of Arrival (DOA) estimation v...
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Recent advancements in array signal processing focus on enhancing source detection and reducing the effects of mutual coupling among array elements. This has been achieved using Direction of Arrival (DOA) estimation via virtual arrays formed by sparse arrays. Non-Redundant arrays (NRAs) are a very common structure among sparse arrays. Traditionally, one-dimensional NRAs capture either azimuth or elevation angles of sources, but practical scenarios often require both simultaneously. This paper introduces optimized methods for designing twodimensional (2-D) NRAs to address this need. In addition to the optimized design approach for creating 2-D NRAs with minimum aperture, the optimized design approaches for creating 2-D NRAs with desired aperture, with minimized mutual coupling effect and with hybrid of both are proposed. The designed arrays can be in the form of a rectangle or a regular polygon with the number of sides being a multiple of 4. The proposed array design methods significantly enhance the flexibility in designing NRAs, allowing the creation of various array configurations for any desired number of sensors. Simulation results show that the proposed arrays outperform the existing 2-D arrays in estimating the DOAs of signal sources and show more robustness against the effects of mutual coupling.
Difference bases are discussed and their relevance to sensor arrays is described. Several new analytical difference base structures that result in near optimal low-redundancy sensor arrays are introduced. Algorithms a...
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Difference bases are discussed and their relevance to sensor arrays is described. Several new analytical difference base structures that result in near optimal low-redundancy sensor arrays are introduced. Algorithms are also presented for efficiently obtaining sparse sensor arrays and/or difference bases. Lastly, new bounds, related to arrays that have both redundancies and holes in their coarray are presented. Also, some extensions to the idea of difference bases that may yield useful results for sensor array design are discussed.
This correspondence improves and extends bounds on the numbers of sensors, redundancies, and holes for sparse linear arrays to sparse planar and volume arrays. As an application, the efficiency of regular planar and v...
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This correspondence improves and extends bounds on the numbers of sensors, redundancies, and holes for sparse linear arrays to sparse planar and volume arrays. As an application, the efficiency of regular planar and volume arrays with redundancies but no holes is deduced. Also, examples of new redundancy and hole square arrays, found by exhaustive computer search, are given.
For direction-of-arrival (DOA) estimation problems, sparse Bayesian learning (SBL) has achieved excellent estimation performance, especially in sparse arrays. However, numerous SBL-based methods with hyper parameters ...
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For direction-of-arrival (DOA) estimation problems, sparse Bayesian learning (SBL) has achieved excellent estimation performance, especially in sparse arrays. However, numerous SBL-based methods with hyper parameters assigned to Gaussian priors cannot enhance sparsity well, and mainly focus on the nested array (NA) or the co-prime array (CPA) that cause relatively large degree of freedom (DOF) losses. Based on this, we propose a novel method with a Bayesian framework containing three-stage hierarchical Laplace priors that significantly promote sparsity. Moreover, the proposed method is based on the minimum hole array (MHA) that retains a larger array aperture than NA or CPA after redundancy removal, which is required and achieved simultaneously by a denoising operation. In addition, to correct the intractable off-grid model errors caused by grid mismatch, a new refinement operation is developed. And, the refinement empirically outperforms others based on Taylor expansion. Extensive simulations are presented to confirm the superiority of the proposed method beyond stateof-the-art methods.
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