FPGA Implementation of Real-Time Compressive Sensing with Partial Fourier Dictionary
This paper presents a novel real-time compressive sensing (CS) reconstruction which employs high density field-programmable gate array (FPGA) for hardware acceleration. Traditionally, CS can be implemented using a high-level computer language in a personal computer (PC) or multicore platforms, such...
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| Format: | Article |
| Language: | English |
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Wiley
2016-01-01
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| Series: | International Journal of Antennas and Propagation |
| Online Access: | http://dx.doi.org/10.1155/2016/1671687 |
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| author | Yinghui Quan Yachao Li Xiaoxiao Gao Mengdao Xing |
| author_facet | Yinghui Quan Yachao Li Xiaoxiao Gao Mengdao Xing |
| author_sort | Yinghui Quan |
| collection | DOAJ |
| description | This paper presents a novel real-time compressive sensing (CS) reconstruction which employs high density field-programmable gate array (FPGA) for hardware acceleration. Traditionally, CS can be implemented using a high-level computer language in a personal computer (PC) or multicore platforms, such as graphics processing units (GPUs) and Digital Signal Processors (DSPs). However, reconstruction algorithms are computing demanding and software implementation of these algorithms is extremely slow and power consuming. In this paper, the orthogonal matching pursuit (OMP) algorithm is refined to solve the sparse decomposition optimization for partial Fourier dictionary, which is always adopted in radar imaging and detection application. OMP reconstruction can be divided into two main stages: optimization which finds the closely correlated vectors and least square problem. For large scale dictionary, the implementation of correlation is time consuming since it often requires a large number of matrix multiplications. Also solving the least square problem always needs a scalable matrix decomposition operation. To solve these problems efficiently, the correlation optimization is implemented by fast Fourier transform (FFT) and the large scale least square problem is implemented by Conjugate Gradient (CG) technique, respectively. The proposed method is verified by FPGA (Xilinx Virtex-7 XC7VX690T) realization, revealing its effectiveness in real-time applications. |
| format | Article |
| id | doaj-art-381a83d7f9be46798cd6a050e04d3d20 |
| institution | Kabale University |
| issn | 1687-5869 1687-5877 |
| language | English |
| publishDate | 2016-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Antennas and Propagation |
| spelling | doaj-art-381a83d7f9be46798cd6a050e04d3d202025-02-03T01:22:17ZengWileyInternational Journal of Antennas and Propagation1687-58691687-58772016-01-01201610.1155/2016/16716871671687FPGA Implementation of Real-Time Compressive Sensing with Partial Fourier DictionaryYinghui Quan0Yachao Li1Xiaoxiao Gao2Mengdao Xing3National Laboratory of Radar Signal Processing, Xidian University, Xi’an 710071, ChinaNational Laboratory of Radar Signal Processing, Xidian University, Xi’an 710071, ChinaNational Laboratory of Radar Signal Processing, Xidian University, Xi’an 710071, ChinaNational Laboratory of Radar Signal Processing, Xidian University, Xi’an 710071, ChinaThis paper presents a novel real-time compressive sensing (CS) reconstruction which employs high density field-programmable gate array (FPGA) for hardware acceleration. Traditionally, CS can be implemented using a high-level computer language in a personal computer (PC) or multicore platforms, such as graphics processing units (GPUs) and Digital Signal Processors (DSPs). However, reconstruction algorithms are computing demanding and software implementation of these algorithms is extremely slow and power consuming. In this paper, the orthogonal matching pursuit (OMP) algorithm is refined to solve the sparse decomposition optimization for partial Fourier dictionary, which is always adopted in radar imaging and detection application. OMP reconstruction can be divided into two main stages: optimization which finds the closely correlated vectors and least square problem. For large scale dictionary, the implementation of correlation is time consuming since it often requires a large number of matrix multiplications. Also solving the least square problem always needs a scalable matrix decomposition operation. To solve these problems efficiently, the correlation optimization is implemented by fast Fourier transform (FFT) and the large scale least square problem is implemented by Conjugate Gradient (CG) technique, respectively. The proposed method is verified by FPGA (Xilinx Virtex-7 XC7VX690T) realization, revealing its effectiveness in real-time applications.http://dx.doi.org/10.1155/2016/1671687 |
| spellingShingle | Yinghui Quan Yachao Li Xiaoxiao Gao Mengdao Xing FPGA Implementation of Real-Time Compressive Sensing with Partial Fourier Dictionary International Journal of Antennas and Propagation |
| title | FPGA Implementation of Real-Time Compressive Sensing with Partial Fourier Dictionary |
| title_full | FPGA Implementation of Real-Time Compressive Sensing with Partial Fourier Dictionary |
| title_fullStr | FPGA Implementation of Real-Time Compressive Sensing with Partial Fourier Dictionary |
| title_full_unstemmed | FPGA Implementation of Real-Time Compressive Sensing with Partial Fourier Dictionary |
| title_short | FPGA Implementation of Real-Time Compressive Sensing with Partial Fourier Dictionary |
| title_sort | fpga implementation of real time compressive sensing with partial fourier dictionary |
| url | http://dx.doi.org/10.1155/2016/1671687 |
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