The performance of an adaptive beamformer is significantly influenced by its array configuration. The problem of optimum array configuration for minimum variance distortionless response (MVDR) beam formers has been re...
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The performance of an adaptive beamformer is significantly influenced by its array configuration. The problem of optimum array configuration for minimum variance distortionless response (MVDR) beam formers has been recently investigated under the assumption of accurate estimate or prior exact knowledge of the source direction of arrival (DOA). Inaccuracies in DOA can lead to significant performance degradation. Improving the robustness of MVDR beamformers has commonly been achieved by adding appropriate constraints in the determination of beamforming weights, such as the linearly constrained minimum variance (LCMV) beamformer. This work examines the sensitivity of different sparsearray configurations towards uncertainty in the source DOA. It proposes enhancing system robustness through optimizing array configurations. The sparsearray design problem is formulated in terms of maximizing the output signal-to-interference-plus-noise ratio (SINR) of the MVDR and LCMV beamformers. The constrained maximization problem is expressed as the fraction of matrix determinants, and a sequential convex programming algorithm is adopted for the solution of the corresponding non-convex problem. Numerical examples are presented to validate the robustness of configured sparsearray MVDR and LCMV beamformers for small errors in source directional angles. (C) 2017 Elsevier B.V. All rights reserved.
In this paper, a sparsearray signal processing algorithm using iterative optimization strategy is proposed to select an optimal sparse-aperiodic subarray in an existing fixed array. The algorithm is based on an Impro...
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ISBN:
(纸本)9781728154466
In this paper, a sparsearray signal processing algorithm using iterative optimization strategy is proposed to select an optimal sparse-aperiodic subarray in an existing fixed array. The algorithm is based on an Improved Genetic Algorithm (IGA) that simultaneously adjusts the inter-senor spacing of a conformal array with arbitrary geometrical configuration. A novel crossover method is developed to improve the convergence performance. The proposed method can achieve lower Peak Sidelobe Level (PSL) using fewer sensors and thereby reduce redundancy. The robustness of the algorithm is also tested using multiple independent simulations.
The location optimization problem for robustsparse antenna array design is addressed with the aim of finding a set of antenna locations that is robust to norm-bounded steering vector errors. A constraint of the anten...
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The location optimization problem for robustsparse antenna array design is addressed with the aim of finding a set of antenna locations that is robust to norm-bounded steering vector errors. A constraint of the antenna's physical size is also imposed to ensure the antennas can fit into the optimized locations. Narrowband and multiband design examples are provided to verify the effectiveness of the proposed design method.
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