The blockorthogonalmatchingpursuit (BOMP) algorithm is an efficient method in compressed sensing (CS) for the reconstruction of block-sparse signals, whose non-zero entries occur in clusters. However, due to the no...
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The blockorthogonalmatchingpursuit (BOMP) algorithm is an efficient method in compressed sensing (CS) for the reconstruction of block-sparse signals, whose non-zero entries occur in clusters. However, due to the non-ideal factors in practice, there exits perturbation in the CS system, which may cause significant performance degradation during reconstruction. In this Letter, a perturbed BOMP algorithm is presented to deal with this problem, which extends BOMP algorithm to the perturbation case. The proposed algorithm performs controlled perturbation on each selected block of support vectors to decrease the orthogonal residual at each iteration. Moreover, the condition of successful reconstruction is derived. Simulation results demonstrate the effectiveness and robustness of the proposed algorithm.
In this study, the problem of Stokes parameters and direction-of-arrival estimation of polarised sources is addressed based on the block-sparsity reconstruction, in case of an unknown number of sources. Since Stokes p...
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In this study, the problem of Stokes parameters and direction-of-arrival estimation of polarised sources is addressed based on the block-sparsity reconstruction, in case of an unknown number of sources. Since Stokes parameters have four components, the block-sparsity model of polarised sources is introduced by employing the difference coarray of the coprime array with cross-dipole sensors. In case of an unknown number of sources, a novel estimate approach is proposed by combining the blockorthogonalmatchingpursuit (BOMP) algorithm with the deterministic maximum likelihood (DML). In the proposed approach, the DML test step and refining grid step are added in each iteration of BOMP to identify the number of sources and to reduce the estimation error incurred by the grid mismatch. This approach has low computational complexity and is suitable for both the completely polarised and partially polarised sources. Simulations are used to verify the performance of the proposed approach.
In order to solve the problems of slow imaging speed and poor reconstruction accuracy of wall parameters under the condition of wall parameter fuzziness, an improved limited Broyden-Fletcher-Goldfarb-Shanno-particle s...
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In order to solve the problems of slow imaging speed and poor reconstruction accuracy of wall parameters under the condition of wall parameter fuzziness, an improved limited Broyden-Fletcher-Goldfarb-Shanno-particle swarm optimisation (LBFGS-PSO) algorithm was proposed. The LBFGS-PSO algorithm model solves the problems of slow calculation speed and large errors of the traditional quasi-Newton algorithm and particle swarm algorithm. The algorithm combined with block orthogonal matching pursuit algorithm can not only accurately reconstruct the position of the sidewall, but also can use the multi-path information to accurately reconstruct the moving target and the stationary target. Compared with the traditional BFGS algorithm and PSO algorithm, the proposed algorithm can reduce the calculation time and provide more accurate estimation results. Simulation results and data analysis verify the performance of the proposed algorithm.
Intelligent terminals often produce a large number of data packets of small lengths. For these packets, it is inefficient to follow the conventional medium access control (MAC) protocols because they lead to poor util...
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ISBN:
(纸本)9781479941452
Intelligent terminals often produce a large number of data packets of small lengths. For these packets, it is inefficient to follow the conventional medium access control (MAC) protocols because they lead to poor utilization of service resources. We propose a novel multiple access scheme that targets massive multiple-input multiple-output (MIMO) systems based on compressive sensing (CS). We employ block precoding in the time domain to enable the simultaneous transmissions of many users, which could be even more than the number of receive antennas at the base station. We develop a block-sparse system model and adopt the blockorthogonalmatchingpursuit (BOMP) algorithm to recover the transmitted signals. Conditions for data recovery guarantees are identified and numerical results demonstrate that our scheme is efficient for uplink small packet transmission.
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